This research focuses on addressing the challenges associated with image detection in low-light environments,particularly by applying artificial intelligence techniques to machine vision and object recognition systems...This research focuses on addressing the challenges associated with image detection in low-light environments,particularly by applying artificial intelligence techniques to machine vision and object recognition systems.The primary goal is to tackle issues related to recognizing objects with low brightness levels.In this study,the Intel RealSense Lidar Camera L515 is used to simultaneously capture color information and 16-bit depth information images.The detection scenarios are categorized into normal brightness and low brightness situations.When the system determines a normal brightness environment,normal brightness images are recognized using deep learning methods.In low-brightness situations,three methods are proposed for recognition.The first method is the SegmentationwithDepth image(SD)methodwhich involves segmenting the depth image,creating amask from the segmented depth image,mapping the obtained mask onto the true color(RGB)image to obtain a backgroundreduced RGB image,and recognizing the segmented image.The second method is theHDVmethod(hue,depth,value)which combines RGB images converted to HSV images(hue,saturation,value)with depth images D to form HDV images for recognition.The third method is the HSD(hue,saturation,depth)method which similarly combines RGB images converted to HSV images with depth images D to form HSD images for recognition.In experimental results,in normal brightness environments,the average recognition rate obtained using image recognition methods is 91%.For low-brightness environments,using the SD method with original images for training and segmented images for recognition achieves an average recognition rate of over 82%.TheHDVmethod achieves an average recognition rate of over 70%,while the HSD method achieves an average recognition rate of over 84%.The HSD method allows for a quick and convenient low-light object recognition system.This research outcome can be applied to nighttime surveillance systems or nighttime road safety systems.展开更多
In standard iris recognition systems,a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture,look-and-stare constraints,and a close distance requ...In standard iris recognition systems,a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture,look-and-stare constraints,and a close distance requirement to the capture device.When these conditions are relaxed,the system’s performance significantly deteriorates due to segmentation and feature extraction problems.Herein,a novel segmentation algorithm is proposed to correctly detect the pupil and limbus boundaries of iris images captured in unconstrained environments.First,the algorithm scans the whole iris image in the Hue Saturation Value(HSV)color space for local maxima to detect the sclera region.The image quality is then assessed by computing global features in red,green and blue(RGB)space,as noisy images have heterogeneous characteristics.The iris images are accordingly classified into seven categories based on their global RGB intensities.After the classification process,the images are filtered,and adaptive thresholding is applied to enhance the global contrast and detect the outer iris ring.Finally,to characterize the pupil area,the algorithm scans the cropped outer ring region for local minima values to identify the darkest area in the iris ring.The experimental results show that our method outperforms existing segmentation techniques using the UBIRIS.v1 and v2 databases and achieved a segmentation accuracy of 99.32 on UBIRIS.v1 and an error rate of 1.59 on UBIRIS.v2.展开更多
The rapid economic growth,urbanization,and industrialization have led to a scarcity of land resources in coastal areas,exacerbating the conflict between humans and the environment.In order to promote economic developm...The rapid economic growth,urbanization,and industrialization have led to a scarcity of land resources in coastal areas,exacerbating the conflict between humans and the environment.In order to promote economic development,attention has turned to the sea,and various coastal engineering projects have been undertaken,sparking a wave of land reclamation.However,while these efforts bring economic and social benefits,they also have implications for ecological relationships.To respond to and plan for changes in the coastline and land cover in a timely manner,this paper proposes and constructs a GIS system that integrates remote sensing image recognition models.The system combines geographic information system development technology with image recognition technology,streamlining the processing and identification of image data.This approach is particularly advantageous for marine management departments in their long-term monitoring and dynamic management of coastal lines,ensuring a more effective and efficient response.展开更多
This study delves into the applications,challenges,and future directions of deep learning techniques in the field of image recognition.Deep learning,particularly Convolutional Neural Networks(CNNs),Recurrent Neural Ne...This study delves into the applications,challenges,and future directions of deep learning techniques in the field of image recognition.Deep learning,particularly Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),and Generative Adversarial Networks(GANs),has become key to enhancing the precision and efficiency of image recognition.These models are capable of processing complex visual data,facilitating efficient feature extraction and image classification.However,acquiring and annotating high-quality,diverse datasets,addressing imbalances in datasets,and model training and optimization remain significant challenges in this domain.The paper proposes strategies for improving data augmentation,optimizing model architectures,and employing automated model optimization tools to address these challenges,while also emphasizing the importance of considering ethical issues in technological advancements.As technology continues to evolve,the application of deep learning in image recognition will further demonstrate its potent capability to solve complex problems,driving society towards more inclusive and diverse development.展开更多
Currently,one of the most severe problems in the agricultural industry is the effect of diseases and pests on global crop production and economic development.Therefore,further research in the field of crop disease and...Currently,one of the most severe problems in the agricultural industry is the effect of diseases and pests on global crop production and economic development.Therefore,further research in the field of crop disease and pest detection is necessary to address the mentioned problem.Aiming to identify the diseased crops and insect pests timely and accurately and perform appropriate prevention measures to reduce the associated losses,this article proposes a Model-Agnostic Meta-Learning(MAML)attention model based on the meta-learning paradigm.The proposed model combines meta-learning with basic learning and adopts an Efficient Channel Attention(ECA)mod-ule.The module follows the local cross-channel interactive strategy of non-dimensional reduction to strengthen the weight parameters corresponding to certain disease characteristics.The proposed meta-learning-based algorithm has the advantage of strong generalization capability and,by integrating the ECA module in the original model,can achieve more efficient detection in new tasks.The proposed model is verified by experiments,and the experimental results show that compared with the original MAML model,the proposed improved MAML-Attention model has a better performance by 1.8–9.31 percentage points in different classification tasks;the maximum accuracy is increased by 1.15–8.2 percentage points.The experimental results verify the strong generalization ability and good robustness of the proposed MAML-Attention model.Compared to the other few-shot methods,the proposed MAML-Attention performs better.展开更多
Congenital heart defect,accounting for about 30%of congenital defects,is the most common one.Data shows that congenital heart defects have seriously affected the birth rate of healthy newborns.In Fetal andNeonatal Car...Congenital heart defect,accounting for about 30%of congenital defects,is the most common one.Data shows that congenital heart defects have seriously affected the birth rate of healthy newborns.In Fetal andNeonatal Cardiology,medical imaging technology(2D ultrasonic,MRI)has been proved to be helpful to detect congenital defects of the fetal heart and assists sonographers in prenatal diagnosis.It is a highly complex task to recognize 2D fetal heart ultrasonic standard plane(FHUSP)manually.Compared withmanual identification,automatic identification through artificial intelligence can save a lot of time,ensure the efficiency of diagnosis,and improve the accuracy of diagnosis.In this study,a feature extraction method based on texture features(Local Binary Pattern LBP and Histogram of Oriented Gradient HOG)and combined with Bag of Words(BOW)model is carried out,and then feature fusion is performed.Finally,it adopts Support VectorMachine(SVM)to realize automatic recognition and classification of FHUSP.The data includes 788 standard plane data sets and 448 normal and abnormal plane data sets.Compared with some other methods and the single method model,the classification accuracy of our model has been obviously improved,with the highest accuracy reaching 87.35%.Similarly,we also verify the performance of the model in normal and abnormal planes,and the average accuracy in classifying abnormal and normal planes is 84.92%.The experimental results show that thismethod can effectively classify and predict different FHUSP and can provide certain assistance for sonographers to diagnose fetal congenital heart disease.展开更多
As the COVID-19 epidemic spread across the globe,people around the world were advised or mandated to wear masks in public places to prevent its spreading further.In some cases,not wearing a mask could result in a fine...As the COVID-19 epidemic spread across the globe,people around the world were advised or mandated to wear masks in public places to prevent its spreading further.In some cases,not wearing a mask could result in a fine.To monitor mask wearing,and to prevent the spread of future epidemics,this study proposes an image recognition system consisting of a camera,an infrared thermal array sensor,and a convolutional neural network trained in mask recognition.The infrared sensor monitors body temperature and displays the results in real-time on a liquid crystal display screen.The proposed system reduces the inefficiency of traditional object detection by providing training data according to the specific needs of the user and by applying You Only Look Once Version 4(YOLOv4)object detection technology,which experiments show has more efficient training parameters and a higher level of accuracy in object recognition.All datasets are uploaded to the cloud for storage using Google Colaboratory,saving human resources and achieving a high level of efficiency at a low cost.展开更多
The monitoring system designed in this paper is on account of YOLOv5(You Only Look Once)to monitor foreign objects on railway tracks and can broadcast the monitoring information to the locomotive in real time.First,th...The monitoring system designed in this paper is on account of YOLOv5(You Only Look Once)to monitor foreign objects on railway tracks and can broadcast the monitoring information to the locomotive in real time.First,the general structure of the system is determined through demand analysis and feasibility analysis,the foreign object intrusion recognition algorithm is designed,and the data set required for foreign object intrusion recognition is made.Secondly,according to the functional demands,the system selects a suitable neural web,and the programming is reasonable.At last,the system is simulated to validate its functionality(identification and classification of track intrusion and determination of a safe operating zone).展开更多
In this paper, artificial intelligence image recognition technology is used to improve the recognition rate of individual domestic fish and reduce the recognition time, aiming at the problem that it is difficult to ea...In this paper, artificial intelligence image recognition technology is used to improve the recognition rate of individual domestic fish and reduce the recognition time, aiming at the problem that it is difficult to easily observe the species and growth of domestic fish in the underwater non-uniform light field environment. First, starting from the image data collected by polarizing imaging technology, this paper uses subpixel convolution reconstruction to enhance the image, uses image translation and fill technology to build the family fish database, builds the Adam-Dropout-CNN (A-D-CNN) network model, and its convolution kernel size is 3 × 3. The maximum pooling was used for downsampling, and the discarding operation was added after the full connection layer to avoid the phenomenon of network overfitting. The adaptive motion estimation algorithm was used to solve the gradient sparse problem. The experiment shows that the recognition rate of A-D-CNN is 96.97% when the model is trained under the domestic fish image database, which solves the problem of low recognition rate and slow recognition speed of domestic fish in non-uniform light field.展开更多
Recently,there has been a notable surge of interest in scientific research regarding spectral images.The potential of these images to revolutionize the digital photography industry,like aerial photography through Unma...Recently,there has been a notable surge of interest in scientific research regarding spectral images.The potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable attention.One encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image classification.As a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent beneficiaries.This paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and vegetables.By meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical use.Additionally,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural context.The findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in agriculture.Nevertheless,we also shed light on the various issues and limitations of working with spectral images.This comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.展开更多
Deep Convolutional Neural Networks(CNNs)have achieved high accuracy in image classification tasks,however,most existing models are trained on high-quality images that are not subject to image degradation.In practice,i...Deep Convolutional Neural Networks(CNNs)have achieved high accuracy in image classification tasks,however,most existing models are trained on high-quality images that are not subject to image degradation.In practice,images are often affected by various types of degradation which can significantly impact the performance of CNNs.In this work,we investigate the influence of image degradation on three typical image classification CNNs and propose a Degradation Type Adaptive Image Classification Model(DTA-ICM)to improve the existing CNNs’classification accuracy on degraded images.The proposed DTA-ICM comprises two key components:a Degradation Type Predictor(DTP)and a Degradation Type Specified Image Classifier(DTS-IC)set,which is trained on existing CNNs for specified types of degradation.The DTP predicts the degradation type of a test image,and the corresponding DTS-IC is then selected to classify the image.We evaluate the performance of both the proposed DTP and the DTA-ICMon the Caltech 101 database.The experimental results demonstrate that the proposed DTP achieves an average accuracy of 99.70%.Moreover,the proposed DTA-ICM,based on AlexNet,VGG19,and ResNet152,exhibits an average accuracy improvement of 20.63%,18.22%,and 12.9%,respectively,compared with the original CNNs in classifying degraded images.It suggests that the proposed DTA-ICM can effectively improve the classification performance of existing CNNs on degraded images,which has important practical implications.展开更多
In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the s...In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is difficult.Recently,deep learning technology has been employed to detect and identify rice diseases and pests.This paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing studies.Finally,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and suggestions.This study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.展开更多
Optical braille recognition methods typically employ existing target detection models or segmentation modelsfor the direct detection and recognition of braille characters in original braille images. However, these met...Optical braille recognition methods typically employ existing target detection models or segmentation modelsfor the direct detection and recognition of braille characters in original braille images. However, these methodsneed improvement in accuracy and generalizability, especially in densely dotted braille image environments. Thispaper presents a two-stage braille recognition framework. The first stage is a braille dot detection algorithmbased on Gaussian diffusion, targeting Gaussian heatmaps generated by the convex dots in braille images. Thisis applied to the detection of convex dots in double-sided braille, achieving high accuracy in determining thecentral coordinates of the braille convex dots. The second stage involves constructing a braille grid using traditionalpost-processing algorithms to recognize braille character information. Experimental results demonstrate that thisframework exhibits strong robustness and effectiveness in detecting braille dots and recognizing braille charactersin complex double-sided braille image datasets. The framework achieved an F1 score of 99.89% for Braille dotdetection and 99.78% for Braille character recognition. Compared to the highest accuracy in existing methods,these represent improvements of 0.08% and 0.02%, respectively.展开更多
Introduction: Ultrafast latest developments in artificial intelligence (ΑΙ) have recently multiplied concerns regarding the future of robotic autonomy in surgery. However, the literature on the topic is still scarce...Introduction: Ultrafast latest developments in artificial intelligence (ΑΙ) have recently multiplied concerns regarding the future of robotic autonomy in surgery. However, the literature on the topic is still scarce. Aim: To test a novel AI commercially available tool for image analysis on a series of laparoscopic scenes. Methods: The research tools included OPENAI CHATGPT 4.0 with its corresponding image recognition plugin which was fed with a list of 100 laparoscopic selected snapshots from common surgical procedures. In order to score reliability of received responses from image-recognition bot, two corresponding scales were developed ranging from 0 - 5. The set of images was divided into two groups: unlabeled (Group A) and labeled (Group B), and according to the type of surgical procedure or image resolution. Results: AI was able to recognize correctly the context of surgical-related images in 97% of its reports. For the labeled surgical pictures, the image-processing bot scored 3.95/5 (79%), whilst for the unlabeled, it scored 2.905/5 (58.1%). Phases of the procedure were commented in detail, after all successful interpretations. With rates 4 - 5/5, the chatbot was able to talk in detail about the indications, contraindications, stages, instrumentation, complications and outcome rates of the operation discussed. Conclusion: Interaction between surgeon and chatbot appears to be an interesting frontend for further research by clinicians in parallel with evolution of its complex underlying infrastructure. In this early phase of using artificial intelligence for image recognition in surgery, no safe conclusions can be drawn by small cohorts with commercially available software. Further development of medically-oriented AI software and clinical world awareness are expected to bring fruitful information on the topic in the years to come.展开更多
This paper introduces an intelligent waste recycling automatic classification system,which integrates sensors,image recognition,and robotic arms to achieve automatic identification and classification of waste.The syst...This paper introduces an intelligent waste recycling automatic classification system,which integrates sensors,image recognition,and robotic arms to achieve automatic identification and classification of waste.The system monitors the composition and properties of waste in real time through sensors,and uses image recognition technology for precise classification,and the robotic arm is responsible for grabbing and disposing.The design and implementation of the system have important practical significance and application value,and help promote the popularization and standardization of waste classification.This paper details the system s architecture,module division,sensors and recognition technology,robotic arm and grabbing technology,data processing and control system,and testing and optimization process.Experimental results show that the system has efficient waste recycling efficiency and accuracy in practical applications,bringing new development opportunities to the waste recycling industry.展开更多
In this paper, an improved Fast-R-CNN nuclear power cold source disaster biological image recognition algorithm is proposed to improve the safety operation of nuclear power plants. Firstly, the image data sets of the ...In this paper, an improved Fast-R-CNN nuclear power cold source disaster biological image recognition algorithm is proposed to improve the safety operation of nuclear power plants. Firstly, the image data sets of the disaster-causing creatures hairy shrimp and jellyfish were established. Then, in order to solve the problems of low recognition accuracy and unrecognizable small entities in disaster biometrics, Gamma correction algorithm was used to optimize the image of the data set, improve the image quality and reduce the noise interference. Transposed convolution is introduced into the convolution layer to increase the recognition accuracy of small targets. The experimental results show that the recognition rate of this algorithm is 6.75%, 7.5%, 9.8% and 9.03% higher than that of ResNet-50, MobileNetv1, GoogleNet and VGG16, respectively. The actual test results show that the accuracy of this algorithm is obviously better than other algorithms, and the recognition efficiency is higher, which basically meets the preset requirements of this paper.展开更多
This paper proposes an intelligent vehicle auxiliary handling system based on Internet of Things(IoT)technology,featuring an innovative holding mechanism design that adjusts to the length and width of various vehicles...This paper proposes an intelligent vehicle auxiliary handling system based on Internet of Things(IoT)technology,featuring an innovative holding mechanism design that adjusts to the length and width of various vehicles.The system combines precise positioning using satellite tracking technology,intelligent recognition via OpenCV,and the interconnectivity of IoT.This intelligent vehicle auxiliary handling system can independently identify vehicle positions and plan optimal handling paths,eliminating the traditional reliance on manual operation.It offers efficient and accurate handling,setting a new trend in the handling industry.Additionally,the system integrates seamlessly with parking lot management systems,providing real-time updates on vehicle locations and statuses.This allows managers to monitor the parking lot operations clearly and efficiently.This intelligent coordination greatly enhances overall work efficiency and streamlines parking management.Overall,the innovative design of the intelligent vehicle auxiliary handling system represents a significant breakthrough in both function and performance,gaining user favor with its smooth operation.Looking ahead,continued technological advancements and the expansion of application fields will bring even more vitality and intelligence to societal development.展开更多
Image recognition has always been a hot research topic in the scientific community and industry.The emergence of convolutional neural networks(CNN)has made this technology turned into research focus on the field of co...Image recognition has always been a hot research topic in the scientific community and industry.The emergence of convolutional neural networks(CNN)has made this technology turned into research focus on the field of computer vision,especially in image recognition.But it makes the recognition result largely dependent on the number and quality of training samples.Recently,DCGAN has become a frontier method for generating images,sounds,and videos.In this paper,DCGAN is used to generate sample that is difficult to collect and proposed an efficient design method of generating model.We combine DCGAN with CNN for the second time.Use DCGAN to generate samples and training in image recognition model,which based by CNN.This method can enhance the classification model and effectively improve the accuracy of image recognition.In the experiment,we used the radar profile as dataset for 4 categories and achieved satisfactory classification performance.This paper applies image recognition technology to the meteorological field.展开更多
With the continuous progress of The Times and the development of technology,the rise of network social media has also brought the“explosive”growth of image data.As one of the main ways of People’s Daily communicati...With the continuous progress of The Times and the development of technology,the rise of network social media has also brought the“explosive”growth of image data.As one of the main ways of People’s Daily communication,image is widely used as a carrier of communication because of its rich content,intuitive and other advantages.Image recognition based on convolution neural network is the first application in the field of image recognition.A series of algorithm operations such as image eigenvalue extraction,recognition and convolution are used to identify and analyze different images.The rapid development of artificial intelligence makes machine learning more and more important in its research field.Use algorithms to learn each piece of data and predict the outcome.This has become an important key to open the door of artificial intelligence.In machine vision,image recognition is the foundation,but how to associate the low-level information in the image with the high-level image semantics becomes the key problem of image recognition.Predecessors have provided many model algorithms,which have laid a solid foundation for the development of artificial intelligence and image recognition.The multi-level information fusion model based on the VGG16 model is an improvement on the fully connected neural network.Different from full connection network,convolutional neural network does not use full connection method in each layer of neurons of neural network,but USES some nodes for connection.Although this method reduces the computation time,due to the fact that the convolutional neural network model will lose some useful feature information in the process of propagation and calculation,this paper improves the model to be a multi-level information fusion of the convolution calculation method,and further recovers the discarded feature information,so as to improve the recognition rate of the image.VGG divides the network into five groups(mimicking the five layers of AlexNet),yet it USES 3*3 filters and combines them as a convolution sequence.Network deeper DCNN,channel number is bigger.The recognition rate of the model was verified by 0RL Face Database,BioID Face Database and CASIA Face Image Database.展开更多
With the development of Deep Convolutional Neural Networks(DCNNs),the extracted features for image recognition tasks have shifted from low-level features to the high-level semantic features of DCNNs.Previous studies h...With the development of Deep Convolutional Neural Networks(DCNNs),the extracted features for image recognition tasks have shifted from low-level features to the high-level semantic features of DCNNs.Previous studies have shown that the deeper the network is,the more abstract the features are.However,the recognition ability of deep features would be limited by insufficient training samples.To address this problem,this paper derives an improved Deep Fusion Convolutional Neural Network(DF-Net)which can make full use of the differences and complementarities during network learning and enhance feature expression under the condition of limited datasets.Specifically,DF-Net organizes two identical subnets to extract features from the input image in parallel,and then a well-designed fusion module is introduced to the deep layer of DF-Net to fuse the subnet’s features in multi-scale.Thus,the more complex mappings are created and the more abundant and accurate fusion features can be extracted to improve recognition accuracy.Furthermore,a corresponding training strategy is also proposed to speed up the convergence and reduce the computation overhead of network training.Finally,DF-Nets based on the well-known ResNet,DenseNet and MobileNetV2 are evaluated on CIFAR100,Stanford Dogs,and UECFOOD-100.Theoretical analysis and experimental results strongly demonstrate that DF-Net enhances the performance of DCNNs and increases the accuracy of image recognition.展开更多
基金the National Science and Technology Council of Taiwan under Grant NSTC 112-2221-E-130-005.
文摘This research focuses on addressing the challenges associated with image detection in low-light environments,particularly by applying artificial intelligence techniques to machine vision and object recognition systems.The primary goal is to tackle issues related to recognizing objects with low brightness levels.In this study,the Intel RealSense Lidar Camera L515 is used to simultaneously capture color information and 16-bit depth information images.The detection scenarios are categorized into normal brightness and low brightness situations.When the system determines a normal brightness environment,normal brightness images are recognized using deep learning methods.In low-brightness situations,three methods are proposed for recognition.The first method is the SegmentationwithDepth image(SD)methodwhich involves segmenting the depth image,creating amask from the segmented depth image,mapping the obtained mask onto the true color(RGB)image to obtain a backgroundreduced RGB image,and recognizing the segmented image.The second method is theHDVmethod(hue,depth,value)which combines RGB images converted to HSV images(hue,saturation,value)with depth images D to form HDV images for recognition.The third method is the HSD(hue,saturation,depth)method which similarly combines RGB images converted to HSV images with depth images D to form HSD images for recognition.In experimental results,in normal brightness environments,the average recognition rate obtained using image recognition methods is 91%.For low-brightness environments,using the SD method with original images for training and segmented images for recognition achieves an average recognition rate of over 82%.TheHDVmethod achieves an average recognition rate of over 70%,while the HSD method achieves an average recognition rate of over 84%.The HSD method allows for a quick and convenient low-light object recognition system.This research outcome can be applied to nighttime surveillance systems or nighttime road safety systems.
基金The authors extend their appreciation to the Arab Open University,Saudi Arabia,for funding this work through AOU research fund No.AOURG-2023-009.
文摘In standard iris recognition systems,a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture,look-and-stare constraints,and a close distance requirement to the capture device.When these conditions are relaxed,the system’s performance significantly deteriorates due to segmentation and feature extraction problems.Herein,a novel segmentation algorithm is proposed to correctly detect the pupil and limbus boundaries of iris images captured in unconstrained environments.First,the algorithm scans the whole iris image in the Hue Saturation Value(HSV)color space for local maxima to detect the sclera region.The image quality is then assessed by computing global features in red,green and blue(RGB)space,as noisy images have heterogeneous characteristics.The iris images are accordingly classified into seven categories based on their global RGB intensities.After the classification process,the images are filtered,and adaptive thresholding is applied to enhance the global contrast and detect the outer iris ring.Finally,to characterize the pupil area,the algorithm scans the cropped outer ring region for local minima values to identify the darkest area in the iris ring.The experimental results show that our method outperforms existing segmentation techniques using the UBIRIS.v1 and v2 databases and achieved a segmentation accuracy of 99.32 on UBIRIS.v1 and an error rate of 1.59 on UBIRIS.v2.
文摘The rapid economic growth,urbanization,and industrialization have led to a scarcity of land resources in coastal areas,exacerbating the conflict between humans and the environment.In order to promote economic development,attention has turned to the sea,and various coastal engineering projects have been undertaken,sparking a wave of land reclamation.However,while these efforts bring economic and social benefits,they also have implications for ecological relationships.To respond to and plan for changes in the coastline and land cover in a timely manner,this paper proposes and constructs a GIS system that integrates remote sensing image recognition models.The system combines geographic information system development technology with image recognition technology,streamlining the processing and identification of image data.This approach is particularly advantageous for marine management departments in their long-term monitoring and dynamic management of coastal lines,ensuring a more effective and efficient response.
文摘This study delves into the applications,challenges,and future directions of deep learning techniques in the field of image recognition.Deep learning,particularly Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),and Generative Adversarial Networks(GANs),has become key to enhancing the precision and efficiency of image recognition.These models are capable of processing complex visual data,facilitating efficient feature extraction and image classification.However,acquiring and annotating high-quality,diverse datasets,addressing imbalances in datasets,and model training and optimization remain significant challenges in this domain.The paper proposes strategies for improving data augmentation,optimizing model architectures,and employing automated model optimization tools to address these challenges,while also emphasizing the importance of considering ethical issues in technological advancements.As technology continues to evolve,the application of deep learning in image recognition will further demonstrate its potent capability to solve complex problems,driving society towards more inclusive and diverse development.
基金supported by the Science and Technology Project of Jilin Provincial Department of Education[JJKH20210346KJ]the Capital Construction funds within the budget of Jilin Province in 2021(Innovation capacity construction)[2021C044-4]the Jilin Province Science and Technology Development Plan Project[20210101185JC].
文摘Currently,one of the most severe problems in the agricultural industry is the effect of diseases and pests on global crop production and economic development.Therefore,further research in the field of crop disease and pest detection is necessary to address the mentioned problem.Aiming to identify the diseased crops and insect pests timely and accurately and perform appropriate prevention measures to reduce the associated losses,this article proposes a Model-Agnostic Meta-Learning(MAML)attention model based on the meta-learning paradigm.The proposed model combines meta-learning with basic learning and adopts an Efficient Channel Attention(ECA)mod-ule.The module follows the local cross-channel interactive strategy of non-dimensional reduction to strengthen the weight parameters corresponding to certain disease characteristics.The proposed meta-learning-based algorithm has the advantage of strong generalization capability and,by integrating the ECA module in the original model,can achieve more efficient detection in new tasks.The proposed model is verified by experiments,and the experimental results show that compared with the original MAML model,the proposed improved MAML-Attention model has a better performance by 1.8–9.31 percentage points in different classification tasks;the maximum accuracy is increased by 1.15–8.2 percentage points.The experimental results verify the strong generalization ability and good robustness of the proposed MAML-Attention model.Compared to the other few-shot methods,the proposed MAML-Attention performs better.
基金supported by Fujian Provincial Science and Technology Major Project(No.2020HZ02014)by the grants from National Natural Science Foundation of Fujian(2021J01133,2021J011404)by the Quanzhou Scientific and Technological Planning Projects(Nos.2018C113R,2019C028R,2019C029R,2019C076R and 2019C099R).
文摘Congenital heart defect,accounting for about 30%of congenital defects,is the most common one.Data shows that congenital heart defects have seriously affected the birth rate of healthy newborns.In Fetal andNeonatal Cardiology,medical imaging technology(2D ultrasonic,MRI)has been proved to be helpful to detect congenital defects of the fetal heart and assists sonographers in prenatal diagnosis.It is a highly complex task to recognize 2D fetal heart ultrasonic standard plane(FHUSP)manually.Compared withmanual identification,automatic identification through artificial intelligence can save a lot of time,ensure the efficiency of diagnosis,and improve the accuracy of diagnosis.In this study,a feature extraction method based on texture features(Local Binary Pattern LBP and Histogram of Oriented Gradient HOG)and combined with Bag of Words(BOW)model is carried out,and then feature fusion is performed.Finally,it adopts Support VectorMachine(SVM)to realize automatic recognition and classification of FHUSP.The data includes 788 standard plane data sets and 448 normal and abnormal plane data sets.Compared with some other methods and the single method model,the classification accuracy of our model has been obviously improved,with the highest accuracy reaching 87.35%.Similarly,we also verify the performance of the model in normal and abnormal planes,and the average accuracy in classifying abnormal and normal planes is 84.92%.The experimental results show that thismethod can effectively classify and predict different FHUSP and can provide certain assistance for sonographers to diagnose fetal congenital heart disease.
文摘As the COVID-19 epidemic spread across the globe,people around the world were advised or mandated to wear masks in public places to prevent its spreading further.In some cases,not wearing a mask could result in a fine.To monitor mask wearing,and to prevent the spread of future epidemics,this study proposes an image recognition system consisting of a camera,an infrared thermal array sensor,and a convolutional neural network trained in mask recognition.The infrared sensor monitors body temperature and displays the results in real-time on a liquid crystal display screen.The proposed system reduces the inefficiency of traditional object detection by providing training data according to the specific needs of the user and by applying You Only Look Once Version 4(YOLOv4)object detection technology,which experiments show has more efficient training parameters and a higher level of accuracy in object recognition.All datasets are uploaded to the cloud for storage using Google Colaboratory,saving human resources and achieving a high level of efficiency at a low cost.
文摘The monitoring system designed in this paper is on account of YOLOv5(You Only Look Once)to monitor foreign objects on railway tracks and can broadcast the monitoring information to the locomotive in real time.First,the general structure of the system is determined through demand analysis and feasibility analysis,the foreign object intrusion recognition algorithm is designed,and the data set required for foreign object intrusion recognition is made.Secondly,according to the functional demands,the system selects a suitable neural web,and the programming is reasonable.At last,the system is simulated to validate its functionality(identification and classification of track intrusion and determination of a safe operating zone).
文摘In this paper, artificial intelligence image recognition technology is used to improve the recognition rate of individual domestic fish and reduce the recognition time, aiming at the problem that it is difficult to easily observe the species and growth of domestic fish in the underwater non-uniform light field environment. First, starting from the image data collected by polarizing imaging technology, this paper uses subpixel convolution reconstruction to enhance the image, uses image translation and fill technology to build the family fish database, builds the Adam-Dropout-CNN (A-D-CNN) network model, and its convolution kernel size is 3 × 3. The maximum pooling was used for downsampling, and the discarding operation was added after the full connection layer to avoid the phenomenon of network overfitting. The adaptive motion estimation algorithm was used to solve the gradient sparse problem. The experiment shows that the recognition rate of A-D-CNN is 96.97% when the model is trained under the domestic fish image database, which solves the problem of low recognition rate and slow recognition speed of domestic fish in non-uniform light field.
文摘Recently,there has been a notable surge of interest in scientific research regarding spectral images.The potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable attention.One encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image classification.As a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent beneficiaries.This paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and vegetables.By meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical use.Additionally,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural context.The findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in agriculture.Nevertheless,we also shed light on the various issues and limitations of working with spectral images.This comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.
基金This work was supported by Special Funds for the Construction of an Innovative Province of Hunan(GrantNo.2020GK2028)lNatural Science Foundation of Hunan Province(Grant No.2022JJ30002)lScientific Research Project of Hunan Provincial EducationDepartment(GrantNo.21B0833)lScientific Research Key Project of Hunan Education Department(Grant No.21A0592)lScientific Research Project of Hunan Provincial Education Department(Grant No.22A0663).
文摘Deep Convolutional Neural Networks(CNNs)have achieved high accuracy in image classification tasks,however,most existing models are trained on high-quality images that are not subject to image degradation.In practice,images are often affected by various types of degradation which can significantly impact the performance of CNNs.In this work,we investigate the influence of image degradation on three typical image classification CNNs and propose a Degradation Type Adaptive Image Classification Model(DTA-ICM)to improve the existing CNNs’classification accuracy on degraded images.The proposed DTA-ICM comprises two key components:a Degradation Type Predictor(DTP)and a Degradation Type Specified Image Classifier(DTS-IC)set,which is trained on existing CNNs for specified types of degradation.The DTP predicts the degradation type of a test image,and the corresponding DTS-IC is then selected to classify the image.We evaluate the performance of both the proposed DTP and the DTA-ICMon the Caltech 101 database.The experimental results demonstrate that the proposed DTP achieves an average accuracy of 99.70%.Moreover,the proposed DTA-ICM,based on AlexNet,VGG19,and ResNet152,exhibits an average accuracy improvement of 20.63%,18.22%,and 12.9%,respectively,compared with the original CNNs in classifying degraded images.It suggests that the proposed DTA-ICM can effectively improve the classification performance of existing CNNs on degraded images,which has important practical implications.
基金funded by Hunan Provincial Natural Science Foundation of China with Grant Numbers(2022JJ50016,2023JJ50096)Innovation Platform Open Fund of Hengyang Normal University Grant 2021HSKFJJ039Hengyang Science and Technology Plan Guiding Project with Number 202222025902.
文摘In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is difficult.Recently,deep learning technology has been employed to detect and identify rice diseases and pests.This paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing studies.Finally,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and suggestions.This study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.
文摘Optical braille recognition methods typically employ existing target detection models or segmentation modelsfor the direct detection and recognition of braille characters in original braille images. However, these methodsneed improvement in accuracy and generalizability, especially in densely dotted braille image environments. Thispaper presents a two-stage braille recognition framework. The first stage is a braille dot detection algorithmbased on Gaussian diffusion, targeting Gaussian heatmaps generated by the convex dots in braille images. Thisis applied to the detection of convex dots in double-sided braille, achieving high accuracy in determining thecentral coordinates of the braille convex dots. The second stage involves constructing a braille grid using traditionalpost-processing algorithms to recognize braille character information. Experimental results demonstrate that thisframework exhibits strong robustness and effectiveness in detecting braille dots and recognizing braille charactersin complex double-sided braille image datasets. The framework achieved an F1 score of 99.89% for Braille dotdetection and 99.78% for Braille character recognition. Compared to the highest accuracy in existing methods,these represent improvements of 0.08% and 0.02%, respectively.
文摘Introduction: Ultrafast latest developments in artificial intelligence (ΑΙ) have recently multiplied concerns regarding the future of robotic autonomy in surgery. However, the literature on the topic is still scarce. Aim: To test a novel AI commercially available tool for image analysis on a series of laparoscopic scenes. Methods: The research tools included OPENAI CHATGPT 4.0 with its corresponding image recognition plugin which was fed with a list of 100 laparoscopic selected snapshots from common surgical procedures. In order to score reliability of received responses from image-recognition bot, two corresponding scales were developed ranging from 0 - 5. The set of images was divided into two groups: unlabeled (Group A) and labeled (Group B), and according to the type of surgical procedure or image resolution. Results: AI was able to recognize correctly the context of surgical-related images in 97% of its reports. For the labeled surgical pictures, the image-processing bot scored 3.95/5 (79%), whilst for the unlabeled, it scored 2.905/5 (58.1%). Phases of the procedure were commented in detail, after all successful interpretations. With rates 4 - 5/5, the chatbot was able to talk in detail about the indications, contraindications, stages, instrumentation, complications and outcome rates of the operation discussed. Conclusion: Interaction between surgeon and chatbot appears to be an interesting frontend for further research by clinicians in parallel with evolution of its complex underlying infrastructure. In this early phase of using artificial intelligence for image recognition in surgery, no safe conclusions can be drawn by small cohorts with commercially available software. Further development of medically-oriented AI software and clinical world awareness are expected to bring fruitful information on the topic in the years to come.
文摘This paper introduces an intelligent waste recycling automatic classification system,which integrates sensors,image recognition,and robotic arms to achieve automatic identification and classification of waste.The system monitors the composition and properties of waste in real time through sensors,and uses image recognition technology for precise classification,and the robotic arm is responsible for grabbing and disposing.The design and implementation of the system have important practical significance and application value,and help promote the popularization and standardization of waste classification.This paper details the system s architecture,module division,sensors and recognition technology,robotic arm and grabbing technology,data processing and control system,and testing and optimization process.Experimental results show that the system has efficient waste recycling efficiency and accuracy in practical applications,bringing new development opportunities to the waste recycling industry.
文摘In this paper, an improved Fast-R-CNN nuclear power cold source disaster biological image recognition algorithm is proposed to improve the safety operation of nuclear power plants. Firstly, the image data sets of the disaster-causing creatures hairy shrimp and jellyfish were established. Then, in order to solve the problems of low recognition accuracy and unrecognizable small entities in disaster biometrics, Gamma correction algorithm was used to optimize the image of the data set, improve the image quality and reduce the noise interference. Transposed convolution is introduced into the convolution layer to increase the recognition accuracy of small targets. The experimental results show that the recognition rate of this algorithm is 6.75%, 7.5%, 9.8% and 9.03% higher than that of ResNet-50, MobileNetv1, GoogleNet and VGG16, respectively. The actual test results show that the accuracy of this algorithm is obviously better than other algorithms, and the recognition efficiency is higher, which basically meets the preset requirements of this paper.
文摘This paper proposes an intelligent vehicle auxiliary handling system based on Internet of Things(IoT)technology,featuring an innovative holding mechanism design that adjusts to the length and width of various vehicles.The system combines precise positioning using satellite tracking technology,intelligent recognition via OpenCV,and the interconnectivity of IoT.This intelligent vehicle auxiliary handling system can independently identify vehicle positions and plan optimal handling paths,eliminating the traditional reliance on manual operation.It offers efficient and accurate handling,setting a new trend in the handling industry.Additionally,the system integrates seamlessly with parking lot management systems,providing real-time updates on vehicle locations and statuses.This allows managers to monitor the parking lot operations clearly and efficiently.This intelligent coordination greatly enhances overall work efficiency and streamlines parking management.Overall,the innovative design of the intelligent vehicle auxiliary handling system represents a significant breakthrough in both function and performance,gaining user favor with its smooth operation.Looking ahead,continued technological advancements and the expansion of application fields will bring even more vitality and intelligence to societal development.
文摘Image recognition has always been a hot research topic in the scientific community and industry.The emergence of convolutional neural networks(CNN)has made this technology turned into research focus on the field of computer vision,especially in image recognition.But it makes the recognition result largely dependent on the number and quality of training samples.Recently,DCGAN has become a frontier method for generating images,sounds,and videos.In this paper,DCGAN is used to generate sample that is difficult to collect and proposed an efficient design method of generating model.We combine DCGAN with CNN for the second time.Use DCGAN to generate samples and training in image recognition model,which based by CNN.This method can enhance the classification model and effectively improve the accuracy of image recognition.In the experiment,we used the radar profile as dataset for 4 categories and achieved satisfactory classification performance.This paper applies image recognition technology to the meteorological field.
文摘With the continuous progress of The Times and the development of technology,the rise of network social media has also brought the“explosive”growth of image data.As one of the main ways of People’s Daily communication,image is widely used as a carrier of communication because of its rich content,intuitive and other advantages.Image recognition based on convolution neural network is the first application in the field of image recognition.A series of algorithm operations such as image eigenvalue extraction,recognition and convolution are used to identify and analyze different images.The rapid development of artificial intelligence makes machine learning more and more important in its research field.Use algorithms to learn each piece of data and predict the outcome.This has become an important key to open the door of artificial intelligence.In machine vision,image recognition is the foundation,but how to associate the low-level information in the image with the high-level image semantics becomes the key problem of image recognition.Predecessors have provided many model algorithms,which have laid a solid foundation for the development of artificial intelligence and image recognition.The multi-level information fusion model based on the VGG16 model is an improvement on the fully connected neural network.Different from full connection network,convolutional neural network does not use full connection method in each layer of neurons of neural network,but USES some nodes for connection.Although this method reduces the computation time,due to the fact that the convolutional neural network model will lose some useful feature information in the process of propagation and calculation,this paper improves the model to be a multi-level information fusion of the convolution calculation method,and further recovers the discarded feature information,so as to improve the recognition rate of the image.VGG divides the network into five groups(mimicking the five layers of AlexNet),yet it USES 3*3 filters and combines them as a convolution sequence.Network deeper DCNN,channel number is bigger.The recognition rate of the model was verified by 0RL Face Database,BioID Face Database and CASIA Face Image Database.
基金This work is partially supported by National Natural Foundation of China(Grant No.61772561)the Key Research&Development Plan of Hunan Province(Grant No.2018NK2012)+2 种基金the Degree&Postgraduate Education Reform Project of Hunan Province(Grant No.2019JGYB154)the Postgraduate Excellent teaching team Project of Hunan Province(Grant[2019]370-133)Teaching Reform Project of Central South University of Forestry and Technology(Grant No.20180682).
文摘With the development of Deep Convolutional Neural Networks(DCNNs),the extracted features for image recognition tasks have shifted from low-level features to the high-level semantic features of DCNNs.Previous studies have shown that the deeper the network is,the more abstract the features are.However,the recognition ability of deep features would be limited by insufficient training samples.To address this problem,this paper derives an improved Deep Fusion Convolutional Neural Network(DF-Net)which can make full use of the differences and complementarities during network learning and enhance feature expression under the condition of limited datasets.Specifically,DF-Net organizes two identical subnets to extract features from the input image in parallel,and then a well-designed fusion module is introduced to the deep layer of DF-Net to fuse the subnet’s features in multi-scale.Thus,the more complex mappings are created and the more abundant and accurate fusion features can be extracted to improve recognition accuracy.Furthermore,a corresponding training strategy is also proposed to speed up the convergence and reduce the computation overhead of network training.Finally,DF-Nets based on the well-known ResNet,DenseNet and MobileNetV2 are evaluated on CIFAR100,Stanford Dogs,and UECFOOD-100.Theoretical analysis and experimental results strongly demonstrate that DF-Net enhances the performance of DCNNs and increases the accuracy of image recognition.