Neural Network in Computer Vision for RoboCup Middle Size League

Robot World Cup Initiative (RoboCup) is a worldwide competition proposed to advance research in robotics and artificial intelligence. It has a league called RoboCup soccer devoted for soccer robots, which is a challenge because robots are mobile, fully autonomous, multi-agents, and they play on a dynamic environment. Moreover, robots must recognize the game entities, which is a crucial task during a game. A camera is usually used as an input system to recognize ball, opponents, soccer field, and so on. These elements may be recognized applying some tools of computational intelligence, for example an artificial neural network. This paper describes the application of an artificial neural network on middle size robotic football league, where a multilayer perceptron neural network is trained with the backpropagation algorithm, to classify elements on the image. Each output neuron represents an entity and its output value depends on the current entity that is present on the image. The results show that an artificial neural network successfully classified the entities. They were recognized even when similar color entities were present on the image.

Model Agnostic Meta-Learning(MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating demand.The presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease management.With advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a challenge.This challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease pathogens.In this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data scarcity.This study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural Networks.This paper also proposes a Model AgnosticMeta Learning(MAML)based ensemble classifier for accurate classification.The proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens detection.In future,more diseases can be included for detection along with some other modalities like pests and weed.

A Novel 6G Scalable Blockchain Clustering-Based Computer Vision Character Detection for Mobile Images

6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous Connectivity.In tandem with these advancements,blockchain technology is leveraged to enhance computer vision applications’security,trustworthiness,and transparency.With the widespread use of mobile devices equipped with cameras,the ability to capture and recognize Chinese characters in natural scenes has become increasingly important.Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount,such as facial recognition or personal healthcare monitoring.Users can control their visual data and grant or revoke access as needed.Recognizing Chinese characters from images can provide convenience in various aspects of people’s lives.However,traditional Chinese character text recognition methods often need higher accuracy,leading to recognition failures or incorrect character identification.In contrast,computer vision technologies have significantly improved image recognition accuracy.This paper proposed a Secure end-to-end recognition system(SE2ERS)for Chinese characters in natural scenes based on convolutional neural networks(CNN)using 6G technology.The proposed SE2ERS model uses the Weighted Hyperbolic Curve Cryptograph(WHCC)of the secure data transmission in the 6G network with the blockchain model.The data transmission within the computer vision system,with a 6G gradient directional histogram(GDH),is employed for character estimation.With the deployment of WHCC and GDH in the constructed SE2ERS model,secure communication is achieved for the data transmission with the 6G network.The proposed SE2ERS compares the performance of traditional Chinese text recognition methods and data transmission environment with 6G communication.Experimental results demonstrate that SE2ERS achieves an average recognition accuracy of 88%for simple Chinese characters,compared to 81.2%with traditional methods.For complex Chinese characters,the average recognition accuracy improves to 84.4%with our system,compared to 72.8%with traditional methods.Additionally,deploying the WHCC model improves data security with the increased data encryption rate complexity of∼12&higher than the traditional techniques.

Exploring Deep Learning Methods for Computer Vision Applications across Multiple Sectors:Challenges and Future Trends

Computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other media.Deep learning(DL)methods are more successful than other traditional machine learning(ML)methods inCV.DL techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face recognition.In this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is presented.The sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and CV.This review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization

Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop a machine vision system for automated coal characterizations.The model was calibrated using 80 image samples that are captured for different coal samples in different angles.All the images were captured in RGB color space and converted into five other color spaces(HSI,CMYK,Lab,xyz,Gray)for feature extraction.The intensity component image of HSI color space was further transformed into four frequency components(discrete cosine transform,discrete wavelet transform,discrete Fourier transform,and Gabor filter)for the texture features extraction.A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development.The datasets of the optimized features were used as an input for the model,and their respective coal characteristics(analyzed in the laboratory)were used as outputs of the model.The R-squared values were found to be 0.89,0.92,0.92,and 0.84,respectively,for fixed carbon,ash content,volatile matter,and moisture content.The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression,support vector regression,and radial basis neural network models.The study demonstrates the potential of the machine vision system in automated coal characterization.

A MACHINE VISION SYSTEM FOR INSPECTING WOOD SURFACE DEFECTS BY USING NEURAL NETWORK

With the development of wood industry, the processing of wood products becomemore significant. This paper discusses the developmen of machine vision system used to inspect andclassny the various types of defects of wood suxface. The surface defeds means the variations ofcolour and textUre. The machine vision system is to dated undesirable 'defecs' that can appear onthe surface of rough wood lwnber. A neural network was used within the Blackboard framework fora labeling verification step of the high-level recognition module of vision system. The system hasbere successfully tested on a number of boards from several different species.

Human and Machine Vision Based Indian Race Classification Using Modified-Convolutional Neural Network

The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographical regions.This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human vision.We have created an Automated Human Intelligence System(AHIS)to evaluate human visual capabilities.Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features.We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy.The proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face shape.Finally,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same dataset.With an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.

Identification of diseases for soybean seeds by computer vision applying BP neural network

The use of computer vision for estimating quality in agriculture products has become wide spread in recent years and the composition,variety,or ripeness can be estimated.On the other hand,the appearance is one of the most worrying issues for producers due to its influence on quality.In this research,computer vision technology combined with BP artificial neural network(ANN)was developed to identify soybean frogeye,mildewed soybean,worm-eaten soybean and damaged soybean.Thirty-nine characteristic parameters from color,texture and shape characteristics were computed after preprocessing the acquired soybean images.The dimensionality of the characteristic parameters was reduced from 39 dimensionalities to 12 dimensionalities using the method of principal component analysis(PCA).MALAB software was used to build a prediction model according to 12 characteristic parameters.The identification accuracies of soybean frogeye,mildewed soybean,damaged soybean and worm-eaten soybean are 96%,95%,92%,and 92%,respectively.And the accuracy for heterogeneous soybean seeds with several diseases is 90%.The results show that the prediction model constructed by BP neural network can identify the diseases of soybean seeds.And it is useful to estimate appearance quality of soybean by computer vision applying BP neural network.

A Computer Vision-Based Model for Automatic Motion Time Study

Motion time study is employed by manufacturing industries to determine operation time.An accurate estimate of operation time is crucial for effective process improvement and production planning.Traditional motion time study is conducted by human analysts with stopwatches,which may be exposed to human errors.In this paper,an automated time study model based on computer vision is proposed.The model integrates a convolutional neural network,which analyzes a video of a manual operation to classify work elements in each video frame,with a time study model that automatically estimates the work element times.An experiment is conducted using a grayscale video and a color video of a manual assembly operation.The work element times from the model are statistically compared to the reference work element time values.The result shows no statistical difference among the time data,which clearly demonstrates the effectiveness of the proposed model.

Dynamic Coordination of Uncalibrated Hand/Eye Robotic System Based on Neural Network

A nonlinear visual mapping model is presented to replace the image Jacobian relation for uncalibrated hand/eye coordination. A new visual tracking controller based on artificial neural network is designed. Simulation results show that this method can drive the static tracking error to zero quickly and keep good robustness and adaptability at the same time. In addition, the algorithm is very easy to be implemented with low computational complexity.

Recognition of mortar pumpability via computer vision and deep learning

The mortar pumpability is essential in the construction industry,which requires much labor to estimate manually and always causes material waste.This paper proposes an effective method by combining a 3-dimensional convolutional neural network(3D CNN)with a 2-dimensional convolutional long short-term memory network(ConvLSTM2D)to automatically classify the mortar pumpability.Experiment results show that the proposed model has an accuracy rate of 100%with a fast convergence speed,based on the dataset organized by collecting the corresponding mortar image sequences.This work demonstrates the feasibility of using computer vision and deep learning for mortar pumpability classification.

Overview of Object Detection Algorithms Using Convolutional Neural Networks

In today’s world, computer vision technology has become a very important direction in the field of Internet applications. As one of the basic problems of computer vision, object detection has become the basis of many vision tasks. Whether we need to realize the interaction between images and text or recognize fine categories, it provides reliable information. This article reviews the development of object detection networks. Starting from RCNN, we introduce object detection based on candidate regions, including Fast R-CNN, Faster R-CNN, etc.;and then start to introduce single-shot networks including YOLO, SSD, and Retina Net, etc. Detectors are the most excellent methods at present. By reviewing the current research status of object detection networks, it provides suggestions for the further development trend and research of object detection.

Automatic Identification of Tomato Maturation Using Multilayer Feed Forward Neural Network with Genetic Algorithms (GA)

We set up computer vision system for tomato images. By using this system, the RGB value of tomato image was converted into HIS value whose H was used to acquire the color character of the surface of tomato. To use multilayer feed forward neural network with GA can finish automatic identification of tomato maturation. The results of experiment showed that the accuracy was up to 94%.

Convolutional Neural Network and Bayesian Gaussian Process in Driving Anger Recognition

With the development of motorization, road traffic crashes have become the leading cause of death in many countries. Among roadway traffic crashes, almost 90% of accidents are related to driver behaviors, wherein driving anger is one of the most leading causes to vehicle crash-related conditions. To some extent, angry driving is considered more dangerous than typical driving distraction due to emotion agitation. Aggressive driving behaviors create many kinds of roadway traffic safety hazards. Mitigating potential risk caused by road rage is essential to increase the overall level of traffic safety. This paper puts forward an integrated computer vision model composed of convolutional neural network in feature extraction and Bayesian Gaussian process in classification to recognize driver anger and distinguish angry driving from natural driving status. Histogram of gradients (HOG) was applied to extract facial features. Convolutional neural network extracted features on eye, eyebrow, and mouth, which are considered most related to anger emotion. Extracted features with its probability were sent to Bayesian Gaussian process classier as input. Integral analysis on three extracted features was conducted by Gaussian process classifier and output returned the likelihood of being anger from the overall study of all extracted features. An overall accuracy rate of 86.2% was achieved in this study. Tongji University 8-Degree-of-Freedom driving simulator was used to collect data from 30 recruited drivers and build test scenario.

Deep Neural Network Based Detection and Segmentation of Ships for Maritime Surveillance

In recent years,computer visionfinds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture.Auto-matic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies.Waterways being an important medium of transport require continuous monitoring for protection of national security.The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea.This paper proposes a deep learning based model capable enough to classify between ships and no-ships as well as to localize ships in the original images using bounding box tech-nique.Furthermore,classified ships are again segmented with deep learning based auto-encoder model.The proposed model,in terms of classification,provides suc-cessful results generating 99.5%and 99.2%validation and training accuracy respectively.The auto-encoder model also produces 85.1%and 84.2%validation and training accuracies.Moreover the IoU metric of the segmented images is found to be of 0.77 value.The experimental results reveal that the model is accu-rate and can be implemented for automatic ship detection in water bodies consid-ering remote sensing satellite images as input to the computer vision system.

Fully Connected Convolutional Neural Network in PCB Soldering Point Inspection

In Electronics Manufacturing Services (EMS) industry, Printed Circuit Board (PCB) inspection is tricky and hard, especially for soldering point inspection due to the extremely tiny size and inconsistent appearance for uneven heating in reflow soldering process. Conventional computer vision technique based on OpenCV or Halcon usually cause false positive call for originally good soldering point on PCB because OpenCV or Halcon use the pre-defined threshold in color proportion for deciding whether the specific soldering point is OK or NG (not good). However, soldering point forms are various after heating in reflow soldering process. This paper puts forward a VGG structure deep convolutional neural network, which is named SolderNet for processing soldering point after reflow heating process to effectively inspect soldering point status, reduce omission rate and error rate, and increase first pass rate. SolderNet consists of 11 hidden convolution layers and 3 densely connected layers. Accuracy reports are divided into OK point recognition and NG point recognition. For OK soldering point recognition, 92% is achieved. For NG soldering point recognition, 99% is achieved. The dataset is collected from KAGA Co. Ltd Plant in Suzhou. First pass rate at KAGA plant is increased from 25% to 80% in general.

Identification of rice seed varieties using neural network

A digital image analysis algorithm based color and morphological features was developed to identify the six varieties （ey7954, syz3, xsl 1, xy5968, xy9308, z903） rice seeds which are widely planted in Zhejiang Province. Seven color and fourteen morphological features were used for discriminant analysis, Two hundred and forty kernels used as the training data set and sixty kernels as the test data set in the neural network used to identify rice seed varieties. When the model was tested on the test data set, the identification accuracies were 90.00%, 88.00%, 95.00%, 82.00%, 74.00%, 80.00% for ey7954, syz3, xsl1, xy5968, xy9308, z903 respectively.

Encoding candlesticks as images for pattern classification using convolutional neural networks

Candlestick charts display the high,low,opening,and closing prices in a specific period.Candlestick patterns emerge because human actions and reactions are patterned and continuously replicate.These patterns capture information on the candles.According to Thomas Bulkowski’s Encyclopedia of Candlestick Charts,there are 103 candlestick patterns.Traders use these patterns to determine when to enter and exit.Candlestick pattern classification approaches take the hard work out of visually identifying these patterns.To highlight its capabilities,we propose a two-steps approach to recognize candlestick patterns automatically.The first step uses the Gramian Angular Field(GAF)to encode the time series as different types of images.The second step uses the Convolutional Neural Network(CNN)with the GAF images to learn eight critical kinds of candlestick patterns.In this paper,we call the approach GAF-CNN.In the experiments,our approach can identify the eight types of candlestick patterns with 90.7%average accuracy automatically in real-world data,outperforming the LSTM model.

Use of Fuzzy Neural Network in Industrial Sorting of Apples

In this paper,an automated system and methodology for nondestructive sorting of apples are presented.Different from the traditional manual grading method,the automated,nondestructive sorting equipment can improve the production efficiency and the grading speed and accuracy.Most popular apple quality detection and grading methods use two-dimensional(2D)machine vision detection based on a single charge-coupled device(CCD)camera detect the external quality.Our system integrates a 3D structured laser into an existing 2D sorting system,which provides the addition third dimension to detect the defects in apples by using the curvature of the structured light strips that are acquired from the optical system of the machine.The curvature of the structured light strip will show the defects in the apple surface.Other features such as color,texture,shape,size and 3D information all play key roles in determining the grade of an apple,which can be determined using a series of feature extraction methods.After feature extraction,a method based on principal component analysis(PCA)for data dimensionality reduction is applied to the system.Furthermore,a comprehensive classification method based on fuzzy neural network(FNN),which is a combination of knowledge-based and model-based method,is used in this paper as the classifier.Preliminary experiments are conducted to verity the feasibility and accuracy of the proposed sorting system.

Color Reproduction on CRT Displays via BP Neural Networks Under Office Environment

A CRT characterization method based on color appearance matching is presented. A matching between Munsell color chips and CRT charts was obtained in vision perceiver in typical office environment and viewing condition by recommending. And neural networks were utilized to accomplish the color space conversion from CIE standard color space to CRT device color space. The neural networks related the color space conversion and color reproduction of soft/hard-copy directly to the influence of the illuminance and viewing condition in vision perceiver. The average color difference of training samples is 3.06 and that of testing samples is 5.17. The experiment results indicated that the neural networks can satisfy the requirements for the color appearance of hard-copy reproduction in CRT.