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IMTNet:Improved Multi-Task Copy-Move Forgery Detection Network with Feature Decoupling and Multi-Feature Pyramid
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作者 Huan Wang Hong Wang +2 位作者 Zhongyuan Jiang Qing Qian Yong Long 《Computers, Materials & Continua》 SCIE EI 2024年第9期4603-4620,共18页
Copy-Move Forgery Detection(CMFD)is a technique that is designed to identify image tampering and locate suspicious areas.However,the practicality of the CMFD is impeded by the scarcity of datasets,inadequate quality a... Copy-Move Forgery Detection(CMFD)is a technique that is designed to identify image tampering and locate suspicious areas.However,the practicality of the CMFD is impeded by the scarcity of datasets,inadequate quality and quantity,and a narrow range of applicable tasks.These limitations significantly restrict the capacity and applicability of CMFD.To overcome the limitations of existing methods,a novel solution called IMTNet is proposed for CMFD by employing a feature decoupling approach.Firstly,this study formulates the objective task and network relationship as an optimization problem using transfer learning.Furthermore,it thoroughly discusses and analyzes the relationship between CMFD and deep network architecture by employing ResNet-50 during the optimization solving phase.Secondly,a quantitative comparison between fine-tuning and feature decoupling is conducted to evaluate the degree of similarity between the image classification and CMFD domains by the enhanced ResNet-50.Finally,suspicious regions are localized using a feature pyramid network with bottom-up path augmentation.Experimental results demonstrate that IMTNet achieves faster convergence,shorter training times,and favorable generalization performance compared to existingmethods.Moreover,it is shown that IMTNet significantly outperforms fine-tuning based approaches in terms of accuracy and F_(1). 展开更多
关键词 Image copy-move detection feature decoupling multi-scale feature pyramids passive forensics
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Two-Layer Attention Feature Pyramid Network for Small Object Detection
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作者 Sheng Xiang Junhao Ma +2 位作者 Qunli Shang Xianbao Wang Defu Chen 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第10期713-731,共19页
Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain les... Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain less information.Many current methods,particularly those based on Feature Pyramid Network(FPN),address this challenge by leveraging multi-scale feature fusion.However,existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers,leading to suboptimal small object detection.To address this problem,we propose the Two-layerAttention Feature Pyramid Network(TA-FPN),featuring two key modules:the Two-layer Attention Module(TAM)and the Small Object Detail Enhancement Module(SODEM).TAM uses the attention module to make the network more focused on the semantic information of the object and fuse it to the lower layer,so that each layer contains similar semantic information,to alleviate the problem of small object information being submerged due to semantic gaps between different layers.At the same time,SODEM is introduced to strengthen the local features of the object,suppress background noise,enhance the information details of the small object,and fuse the enhanced features to other feature layers to ensure that each layer is rich in small object information,to improve small object detection accuracy.Our extensive experiments on challenging datasets such as Microsoft Common Objects inContext(MSCOCO)and Pattern Analysis Statistical Modelling and Computational Learning,Visual Object Classes(PASCAL VOC)demonstrate the validity of the proposedmethod.Experimental results show a significant improvement in small object detection accuracy compared to state-of-theart detectors. 展开更多
关键词 Small object detection two-layer attention module small object detail enhancement module feature pyramid network
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YOLO-VSI: An Improved YOLOv8 Model for Detecting Railway Turnouts Defects in Complex Environments
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作者 Chenghai Yu Zhilong Lu 《Computers, Materials & Continua》 SCIE EI 2024年第11期3261-3280,共20页
Railway turnouts often develop defects such as chipping,cracks,and wear during use.If not detected and addressed promptly,these defects can pose significant risks to train operation safety and passenger security.Despi... Railway turnouts often develop defects such as chipping,cracks,and wear during use.If not detected and addressed promptly,these defects can pose significant risks to train operation safety and passenger security.Despite advances in defect detection technologies,research specifically targeting railway turnout defects remains limited.To address this gap,we collected images from railway inspectors and constructed a dataset of railway turnout defects in complex environments.To enhance detection accuracy,we propose an improved YOLOv8 model named YOLO-VSS-SOUP-Inner-CIoU(YOLO-VSI).The model employs a state-space model(SSM)to enhance the C2f module in the YOLOv8 backbone,proposed the C2f-VSS module to better capture long-range dependencies and contextual features,thus improving feature extraction in complex environments.In the network’s neck layer,we integrate SPDConv and Omni-Kernel Network(OKM)modules to improve the original PAFPN(Path Aggregation Feature Pyramid Network)structure,and proposed the Small Object Upgrade Pyramid(SOUP)structure to enhance small object detection capabilities.Additionally,the Inner-CIoU loss function with a scale factor is applied to further enhance the model’s detection capabilities.Compared to the baseline model,YOLO-VSI demonstrates a 3.5%improvement in average precision on our railway turnout dataset,showcasing increased accuracy and robustness.Experiments on the public NEU-DET dataset reveal a 2.3%increase in average precision over the baseline,indicating that YOLO-VSI has good generalization capabilities. 展开更多
关键词 YOLO railway turnouts defect detection mamba FPN(Feature pyramid Network)
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Dual Attention Based Feature Pyramid Network 被引量:4
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作者 Huijun Xing Shuai Wang +1 位作者 Dezhi Zheng Xiaotong Zhao 《China Communications》 SCIE CSCD 2020年第8期242-252,共11页
Object detection could be recognized as an essential part of the research to scenarios such as automatic driving and pedestrian detection, etc. Among multiple types of target objects, the identification of small-scale... Object detection could be recognized as an essential part of the research to scenarios such as automatic driving and pedestrian detection, etc. Among multiple types of target objects, the identification of small-scale objects faces significant challenges. We would introduce a new feature pyramid framework called Dual Attention based Feature Pyramid Network(DAFPN), which is designed to avoid predicament about multi-scale object recognition. In DAFPN, the attention mechanism is introduced by calculating the topdown pathway and lateral pathway, where the spatial attention, as well as channel attention, would participate, respectively, such that the pyramidal feature maps can be generated with enhanced spatial and channel interdependencies, which bring more semantical information for the feature pyramid. Using the COCO data set, which consists of a considerable quantity of small-scale objects, the experiments are implemented. The analysis results verify the optimized performance of DAFPN compared with the original Feature Pyramid Network(FPN) specifically for the identification on a small scale. The proposed DAFPN is promising for object detection in an era full of intelligent machines that need to detect multi-scale objects. 展开更多
关键词 object detection convolutional neural networks feature pyramid
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Bidirectional parallel multi-branch convolution feature pyramid network for target detection in aerial images of swarm UAVs 被引量:3
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作者 Lei Fu Wen-bin Gu +3 位作者 Wei Li Liang Chen Yong-bao Ai Hua-lei Wang 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2021年第4期1531-1541,共11页
In this paper,based on a bidirectional parallel multi-branch feature pyramid network(BPMFPN),a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swa... In this paper,based on a bidirectional parallel multi-branch feature pyramid network(BPMFPN),a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swarm unmanned aerial vehicles(UAVs).First,the bidirectional parallel multi-branch convolution modules are used to construct the feature pyramid to enhance the feature expression abilities of different scale feature layers.Next,the feature pyramid is integrated into the single-stage object detection framework to ensure real-time performance.In order to validate the effectiveness of the proposed algorithm,experiments are conducted on four datasets.For the PASCAL VOC dataset,the proposed algorithm achieves the mean average precision(mAP)of 85.4 on the VOC 2007 test set.With regard to the detection in optical remote sensing(DIOR)dataset,the proposed algorithm achieves 73.9 mAP.For vehicle detection in aerial imagery(VEDAI)dataset,the detection accuracy of small land vehicle(slv)targets reaches 97.4 mAP.For unmanned aerial vehicle detection and tracking(UAVDT)dataset,the proposed BPMFPN Det achieves the mAP of 48.75.Compared with the previous state-of-the-art methods,the results obtained by the proposed algorithm are more competitive.The experimental results demonstrate that the proposed algorithm can effectively solve the problem of real-time detection of ground multi-scale targets in aerial images of swarm UAVs. 展开更多
关键词 Aerial images Object detection Feature pyramid networks Multi-scale feature fusion Swarm UAVs
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Neighborhood fusion-based hierarchical parallel feature pyramid network for object detection 被引量:3
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作者 Mo Lingfei Hu Shuming 《Journal of Southeast University(English Edition)》 EI CAS 2020年第3期252-263,共12页
In order to improve the detection accuracy of small objects,a neighborhood fusion-based hierarchical parallel feature pyramid network(NFPN)is proposed.Unlike the layer-by-layer structure adopted in the feature pyramid... In order to improve the detection accuracy of small objects,a neighborhood fusion-based hierarchical parallel feature pyramid network(NFPN)is proposed.Unlike the layer-by-layer structure adopted in the feature pyramid network(FPN)and deconvolutional single shot detector(DSSD),where the bottom layer of the feature pyramid network relies on the top layer,NFPN builds the feature pyramid network with no connections between the upper and lower layers.That is,it only fuses shallow features on similar scales.NFPN is highly portable and can be embedded in many models to further boost performance.Extensive experiments on PASCAL VOC 2007,2012,and COCO datasets demonstrate that the NFPN-based SSD without intricate tricks can exceed the DSSD model in terms of detection accuracy and inference speed,especially for small objects,e.g.,4%to 5%higher mAP(mean average precision)than SSD,and 2%to 3%higher mAP than DSSD.On VOC 2007 test set,the NFPN-based SSD with 300×300 input reaches 79.4%mAP at 34.6 frame/s,and the mAP can raise to 82.9%after using the multi-scale testing strategy. 展开更多
关键词 computer vision deep convolutional neural network object detection hierarchical parallel feature pyramid network multi-scale feature fusion
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An Improved Data-Driven Topology Optimization Method Using Feature Pyramid Networks with Physical Constraints 被引量:1
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作者 Jiaxiang Luo Yu Li +3 位作者 Weien Zhou ZhiqiangGong Zeyu Zhang Wen Yao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2021年第9期823-848,共26页
Deep learning for topology optimization has been extensively studied to reduce the cost of calculation in recent years.However,the loss function of the above method is mainly based on pixel-wise errors from the image ... Deep learning for topology optimization has been extensively studied to reduce the cost of calculation in recent years.However,the loss function of the above method is mainly based on pixel-wise errors from the image perspective,which cannot embed the physical knowledge of topology optimization.Therefore,this paper presents an improved deep learning model to alleviate the above difficulty effectively.The feature pyramid network(FPN),a kind of deep learning model,is trained to learn the inherent physical law of topology optimization itself,of which the loss function is composed of pixel-wise errors and physical constraints.Since the calculation of physical constraints requires finite element analysis(FEA)with high calculating costs,the strategy of adjusting the time when physical constraints are added is proposed to achieve the balance between the training cost and the training effect.Then,two classical topology optimization problems are investigated to verify the effectiveness of the proposed method.The results show that the developed model using a small number of samples can quickly obtain the optimization structure without any iteration,which has not only high pixel-wise accuracy but also good physical performance. 展开更多
关键词 Topology optimization deep learning feature pyramid networks finite element analysis physical constraints
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A Model for Helmet-Wearing Detection of Non-Motor Drivers Based on YOLOv5s 被引量:2
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作者 Hongyu Lin Feng Jiang +3 位作者 Yu Jiang Huiyin Luo Jian Yao Jiaxin Liu 《Computers, Materials & Continua》 SCIE EI 2023年第6期5321-5336,共16页
Detecting non-motor drivers’helmets has significant implications for traffic control.Currently,most helmet detection methods are susceptible to the complex background and need more accuracy and better robustness of s... Detecting non-motor drivers’helmets has significant implications for traffic control.Currently,most helmet detection methods are susceptible to the complex background and need more accuracy and better robustness of small object detection,which are unsuitable for practical application scenar-ios.Therefore,this paper proposes a new helmet-wearing detection algorithm based on the You Only Look Once version 5(YOLOv5).First,the Dilated convolution In Coordinate Attention(DICA)layer is added to the backbone network.DICA combines the coordinated attention mechanism with atrous convolution to replace the original convolution layer,which can increase the perceptual field of the network to get more contextual information.Also,it can reduce the network’s learning of unnecessary features in the background and get attention to small objects.Second,the Rebuild Bidirectional Feature Pyramid Network(Re-BiFPN)is used as a feature extraction network.Re-BiFPN uses cross-scale feature fusion to combine the semantic information features at the high level with the spatial information features at the bottom level,which facilitates the model to learn object features at different scales.Verified on the proposed“Helmet Wearing dataset for Non-motor Drivers(HWND),”the results show that the proposed model is superior to the current detection algorithms,with the mean average precision(mAP)of 94.3%under complex background. 展开更多
关键词 Helmet-wearing detection dilated convolution feature pyramid network feature fusion
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AHLNet:Adaptive Multihead Structure and Lightweight Feature Pyramid Network for Detection of Live Working in Substations
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作者 Mengle Peng Xiaoyong Jiang +3 位作者 Langyue Huang Zhongyi Li Haiteng Wu Xiaotang Geng 《Machine Intelligence Research》 EI CSCD 2024年第5期983-992,共10页
With the increasing demand for power in society,there is much live equipment in substations,and the safety and standardization of live working of workers are facing challenges.Aiming at these problems of scene complex... With the increasing demand for power in society,there is much live equipment in substations,and the safety and standardization of live working of workers are facing challenges.Aiming at these problems of scene complexity and object diversity in the real-time detection of the live working safety of substation workers,an adaptive multihead structure and lightweight feature pyramid-based network(AHLNet)is proposed in this study,which is based on YOLOV3.First,we take AH-Darknet53 as the backbone network of YOLOV3,which can introduce an adaptive multihead(AMH)structure,reduce the number of network parameters,and improve the feature extraction ability of the backbone network.Second,to reduce the number of convolution layers of the deeper feature map,a lightweight feature pyramid network(LFPN)is proposed,which can perform feature fusion in advance to alleviate the problem of feature imbalance and gradient disappearance.Finally,the proposed AHLNet is evaluated on the datasets of 16 categories of substation safety operation scenarios,and the average prediction accuracy MAP_(50)reaches 82.10%.Compared with YOLOV3,MAP_(50)is increased by 2.43%,and the number of parameters is 90 M,which is only 38%of the number of parameters of YOLOV3.In addition,the detection speed is basically the same as that of YOLOV3,which can meet the real-time and accurate detection requirements for the safe operation of substation staff. 展开更多
关键词 Adaptive multihead structure lightweight feature pyramid substation feature imbalance multiobject detection
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Gabor-CNN for object detection based on small samples 被引量:4
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作者 Xiao-dong Hu Xin-qing Wang +5 位作者 Fan-jie Meng Xia Hua Yu-ji Yan Yu-yang Li Jing Huang Xun-lin Jiang 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2020年第6期1116-1129,共14页
Object detection models based on convolutional neural networks(CNN)have achieved state-of-the-art performance by heavily rely on large-scale training samples.They are insufficient when used in specific applications,su... Object detection models based on convolutional neural networks(CNN)have achieved state-of-the-art performance by heavily rely on large-scale training samples.They are insufficient when used in specific applications,such as the detection of military objects,as in these instances,a large number of samples is hard to obtain.In order to solve this problem,this paper proposes the use of Gabor-CNN for object detection based on a small number of samples.First of all,a feature extraction convolution kernel library composed of multi-shape Gabor and color Gabor is constructed,and the optimal Gabor convolution kernel group is obtained by means of training and screening,which is convolved with the input image to obtain feature information of objects with strong auxiliary function.Then,the k-means clustering algorithm is adopted to construct several different sizes of anchor boxes,which improves the quality of the regional proposals.We call this regional proposal process the Gabor-assisted Region Proposal Network(Gabor-assisted RPN).Finally,the Deeply-Utilized Feature Pyramid Network(DU-FPN)method is proposed to strengthen the feature expression of objects in the image.A bottom-up and a topdown feature pyramid is constructed in ResNet-50 and feature information of objects is deeply utilized through the transverse connection and integration of features at various scales.Experimental results show that the method proposed in this paper achieves better results than the state-of-art contrast models on data sets with small samples in terms of accuracy and recall rate,and thus has a strong application prospect. 展开更多
关键词 Deep learning Convolutional neural network Small samples Gabor convolution kernel Feature pyramid
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SPM-IS: An auto-algorithm to acquire a mature soybean phenotype based on instance segmentation 被引量:3
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作者 Shuai Li Zhuangzhuang Yan +8 位作者 Yixin Guo Xiaoyan Su Yangyang Cao Bofeng Jiang Fei Yang Zhanguo Zhang Dawei Xin Qingshan Chen Rongsheng Zhu 《The Crop Journal》 SCIE CSCD 2022年第5期1412-1423,共12页
Mature soybean phenotyping is an important process in soybean breeding;however, the manual process is time-consuming and labor-intensive. Therefore, a novel approach that is rapid, accurate and highly precise is requi... Mature soybean phenotyping is an important process in soybean breeding;however, the manual process is time-consuming and labor-intensive. Therefore, a novel approach that is rapid, accurate and highly precise is required to obtain the phenotypic data of soybean stems, pods and seeds. In this research, we propose a mature soybean phenotype measurement algorithm called Soybean Phenotype Measure-instance Segmentation(SPM-IS). SPM-IS is based on a feature pyramid network, Principal Component Analysis(PCA) and instance segmentation. We also propose a new method that uses PCA to locate and measure the length and width of a target object via image instance segmentation. After 60,000 iterations, the maximum mean Average Precision(m AP) of the mask and box was able to reach 95.7%. The correlation coefficients R^(2) of the manual measurement and SPM-IS measurement of the pod length, pod width, stem length, complete main stem length, seed length and seed width were 0.9755, 0.9872, 0.9692, 0.9803,0.9656, and 0.9716, respectively. The correlation coefficients R^(2) of the manual counting and SPM-IS counting of pods, stems and seeds were 0.9733, 0.9872, and 0.9851, respectively. The above results show that SPM-IS is a robust measurement and counting algorithm that can reduce labor intensity, improve efficiency and speed up the soybean breeding process. 展开更多
关键词 SOYBEAN Feature pyramid network PCA Instance segmentation Deep learning
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Soybean Leaf Morphology Classification Based on FPN-SSD and Knowledge Distillation 被引量:2
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作者 Yu Xiao Fu Li-ren +1 位作者 Dai Bai-sheng Wang Ye-cheng 《Journal of Northeast Agricultural University(English Edition)》 CAS 2020年第4期9-17,共9页
Soybean leaf morphology is one of the most important morphological and biological characteristics of soybean.The germplasm gene differences of soybeans can lead to different phenotypic traits,among which soybean leaf ... Soybean leaf morphology is one of the most important morphological and biological characteristics of soybean.The germplasm gene differences of soybeans can lead to different phenotypic traits,among which soybean leaf morphology is an important parameter that directly reflects the difference in soybean germplasm.To realize the morphological classification of soybean leaves,a method was proposed based on deep learning to automatically detect soybean leaves and classify leaf morphology.The morphology of soybean leaves included lanceolate,oval,ellipse and round.First,an image collection platform was designed to collect images of soybean leaves.Then,the feature pyramid networks–single shot multibox detector(FPN-SSD)model was proposed to detect the top leaflets of soybean leaves on the collected images.Finally,a classification model based on knowledge distillation was proposed to classify different morphologies of soybean leaves.The obtained results indicated an overall classification accuracy of 0.956 over a private dataset of 3200 soybean leaf images,and the accuracy of classification for each morphology was 1.00,0.97,0.93 and 0.94.The results showed that this method could effectively classify soybean leaf morphology and had great application potential in analyzing other phenotypic traits of soybean. 展开更多
关键词 leaf morphology classification feature pyramid networks-single shot multibox detector(FPN-SSD) knowledge distillation top leaflet detection
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Detection of Multiscale Center Point Objects Based on Parallel Network 被引量:1
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作者 Hao Chen Hong Zheng Xiaolong Li 《Journal of Artificial Intelligence and Technology》 2021年第1期68-73,共6页
Anchor-based detectors are widely used in object detection.To improve the accuracy of object detection,multiple anchor boxes are intensively placed on the input image,yet.Most of which are invalid.Although the anchor-... Anchor-based detectors are widely used in object detection.To improve the accuracy of object detection,multiple anchor boxes are intensively placed on the input image,yet.Most of which are invalid.Although the anchor-free method can reduce the number of useless anchor boxes,the invalid ones still occupy a high proportion.On this basis,this paper proposes a multiscale center point object detection method based on parallel network to further reduce the number of useless anchor boxes.This study adopts the parallel network architecture of hourglass-104 and darknet-53 of which the first one outputs heatmaps to generate the center point for object feature location on the output attribute feature map of darknet-53.Combining feature pyramid and CIoU loss function,this algorithm is trained and tested on MSCOCO dataset,increasing the detection rate of target location and the accuracy rate of small object detection.Though resembling the state-of-the-art two-stage detectors in overall object detection accuracy,this algorithm is superior in speed. 展开更多
关键词 deep learning heatmap feature pyramid networks object detection center point
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Improved YOLOv7 Algorithm for Floating Waste Detection Based on GFPN and Long-Range Attention Mechanism
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作者 PENG Cheng HE Bing +1 位作者 XI Wenqiang LIN Guancheng 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2024年第4期338-348,共11页
Floating wastes in rivers have specific characteristics such as small scale,low pixel density and complex backgrounds.These characteristics make it prone to false and missed detection during image analysis,thus result... Floating wastes in rivers have specific characteristics such as small scale,low pixel density and complex backgrounds.These characteristics make it prone to false and missed detection during image analysis,thus resulting in a degradation of detection performance.In order to tackle these challenges,a floating waste detection algorithm based on YOLOv7 is proposed,which combines the improved GFPN(Generalized Feature Pyramid Network)and a long-range attention mechanism.Firstly,we import the improved GFPN to replace the Neck of YOLOv7,thus providing more effective information transmission that can scale into deeper networks.Secondly,the convolution-based and hardware-friendly long-range attention mechanism is introduced,allowing the algorithm to rapidly generate an attention map with a global receptive field.Finally,the algorithm adopts the WiseIoU optimization loss function to achieve adaptive gradient gain allocation and alleviate the negative impact of low-quality samples on the gradient.The simulation results reveal that the proposed algorithm has achieved a favorable average accuracy of 86.3%in real-time scene detection tasks.This marks a significant enhancement of approximately 6.3%compared with the baseline,indicating the algorithm's good performance in floating waste detection. 展开更多
关键词 floating waste detection YOLOv7 GFPN(Generalized Feature pyramid Network) long-range attention
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基于YOLOv8改进的脑癌检测算法
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作者 王喆 赵慧俊 +2 位作者 谭超 李骏 申冲 《计算机科学》 2024年第S02期444-450,共7页
自动检测脑部肿瘤在磁共振成像中的位置是一个复杂、繁重的任务,需要耗费大量时间和资源。传统识别方案经常出现误解、遗漏和误导的情况,从而影响患者的治疗进度,对患者的生命安全产生影响。为了进一步提高鉴定的效果,引入了4项关键改... 自动检测脑部肿瘤在磁共振成像中的位置是一个复杂、繁重的任务,需要耗费大量时间和资源。传统识别方案经常出现误解、遗漏和误导的情况,从而影响患者的治疗进度,对患者的生命安全产生影响。为了进一步提高鉴定的效果,引入了4项关键改进措施。首先,采用了高效的多尺度注意力EMA(Efficient Multi-scale Attention),这种方法既可以对全局信息进行编码,也可以对信息进行重新校准,同时通过并行的分支输出特征进行跨维度的交互,使信息进一步聚合。其次,引入了BiFPN(Bidirectional Feature Pyramid Network)模块,并对其结构进行改进,以便缩短每一次检测所需要的时间,同时提升图像识别效果。然后采用MDPIoU损失函数和Mish激活函数进行改进,进一步提高检测的准确度。最后进行仿真实验,实验结果表明,改进的YOLOv8算法在脑癌检测中的精确率、召回率、平均精度均值均有提升,其中Precision提高了4.48%,Recall提高了2.64%,mAP@0.5提高了2.6%,mAP@0.5:0.9提高了7.0%。 展开更多
关键词 YOLOv8 脑癌 Efficient Multi-Scale Attention模块 Bidirectional Feature pyramid Network结构 Missed Softplus with Identity Shortcut激活函数 Minimum Point Distance Intersection over Union损失函数
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Parallel channel and position attention-guided feature pyramid for pig face posture detection
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作者 Zhiwei Hu Hongwen Yan Tiantian Lou 《International Journal of Agricultural and Biological Engineering》 SCIE CAS 2022年第6期222-234,共13页
The area of the pig’s face contains rich biological information,such as eyes,nose,and ear.The high-precision detection of pig face postures is crucial to the identification of pigs,and it can also provide fundamental... The area of the pig’s face contains rich biological information,such as eyes,nose,and ear.The high-precision detection of pig face postures is crucial to the identification of pigs,and it can also provide fundamental archival information for the study of abnormal behavioral characteristics and regularities.In this study,a series of attention blocks were embedded in Feature Pyramid Network(FPN)for automatic detection of the pig face posture in group-breeding environments.Firstly,the Channel Attention Block(CAB)and Position Attention Block(PAB)were proposed to capture the channel dependencies and the pixel-level long-range relationships,respectively.Secondly,a variety of attention modules are proposed to effectively combine the two kinds of attention information,specifically including Parallel Channel Position(PCP),Cascade Position Channel(CPC),and Cascade Channel Position(CCP),which fuse the channel and position attention information in both parallel or cascade ways.Finally,the verification experiments on three task networks with two backbone networks were conducted for different attention blocks or modules.A total of 45 pigs in 8 pigpens were used as the research objects.Experimental results show that attention-based models perform better.Especially,with Faster Region Convolutional Neural Network(Faster R-CNN)as the task network and ResNet101 as the backbone network,after the introduction of the PCP module,the Average Precision(AP)indicators of the face poses of Downward with head-on face(D-O),Downward with lateral face(D-L),Level with head-on face(L-O),Level with lateral face(L-L),Upward with head-on face(U-O),and Upward with lateral face(U-L)achieve 91.55%,90.36%,90.10%,90.05%,85.96%,and 87.92%,respectively.Ablation experiments show that the PAB attention block is not as effective as the CAB attention block,and the parallel combination method is better than the cascade manner.Taking Faster R-CNN as the task network and ResNet101 as the backbone network,the heatmap visualization of different layers of FPN before and after adding PCP shows that,compared with the non-PCP module,the PCP module can more easily aggregate denser and richer contextual information,this,in turn,enhances long-range dependencies to improve feature representation.At the same time,the model based on PCP attention can effectively detect the pig face posture of different ages,different scenes,and different light intensities,which can help lay the foundation for subsequent individual identification and behavior analysis of pigs. 展开更多
关键词 objection detection attention mechanism feature pyramid network face posture detection PIG
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Face anti-spoofing based on multi-modal and multi-scale features fusion
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作者 Kong Chao Ou Weihua +4 位作者 Gong Xiaofeng Li Weian Han Jie Yao Yi Xiong Jiahao 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2022年第6期73-82,共10页
Face anti-spoofing is used to assist face recognition system to judge whether the detected face is real face or fake face. In the traditional face anti-spoofing methods, features extracted by hand are used to describe... Face anti-spoofing is used to assist face recognition system to judge whether the detected face is real face or fake face. In the traditional face anti-spoofing methods, features extracted by hand are used to describe the difference between living face and fraudulent face. But these handmade features do not apply to different variations in an unconstrained environment. The convolutional neural network(CNN) for face deceptions achieves considerable results. However, most existing neural network-based methods simply use neural networks to extract single-scale features from single-modal data, while ignoring multi-scale and multi-modal information. To address this problem, a novel face anti-spoofing method based on multi-modal and multi-scale features fusion(MMFF) is proposed. Specifically, first residual network(Resnet)-34 is adopted to extract features of different scales from each modality, then these features of different scales are fused by feature pyramid network(FPN), finally squeeze-and-excitation fusion(SEF) module and self-attention network(SAN) are combined to fuse features from different modalities for classification. Experiments on the CASIA-SURF dataset show that the new method based on MMFF achieves better performance compared with most existing methods. 展开更多
关键词 face anti-spoofing multi-modal fusion multi-scale fusion self-attention network(SAN) feature pyramid network(FPN)
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Litchi detection in the field using an improved YOLOv3 model 被引量:2
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作者 Hongxing Peng Chao Xue +6 位作者 Yuanyuan Shao Keyin Chen Huanai Liu Juntao Xiong Hu Chen Zongmei Gao Zhengang Yang 《International Journal of Agricultural and Biological Engineering》 SCIE CAS 2022年第2期211-220,共10页
Due to the illumination,complex background,and occlusion of the litchi fruits,the accurate detection of litchi in the field is extremely challenging.In order to solve the problem of the low recognition rate of litchi-... Due to the illumination,complex background,and occlusion of the litchi fruits,the accurate detection of litchi in the field is extremely challenging.In order to solve the problem of the low recognition rate of litchi-picking robots in field conditions,this study was inspired by the ideas of ResNet and dense convolution and proposed an improved feature-extraction network model named“YOLOv3_Litchi”,combining dense connections and residuals for the detection of litchis.Firstly,based on the traditional YOLOv3 deep convolution neural network and regression detection,the idea of residuals was to be put into the feature-extraction network to effectively avoid the problem of decreasing detection accuracy due to the excessive depths of the network layers.Secondly,under the premise of a good receptive field and high detection accuracy,the large convolution kernel was replaced by a small convolution kernel in the shallow layer of the network,thereby effectively reducing the model parameters.Finally,the idea of feature pyramid was used to design the network to identify the small target litchi to ensure that the shallow features were not lost and simultaneously reduced the model parameters.Experimental results show that the improved YOLOv3_Litchi model achieved better results than the classic YOLOv3_DarkNet-53 model and the YOLOv3_Tiny model.The mean average precision(mAP)score was 97.07%,which was higher than the 95.18%mAP of the YOLOv3_DarkNet-53 model and the 94.48%mAP of the YOLOv3_Tiny model.The frame frequency was 58 fps,which was higher than 29 fps of the YOLOv3_DarkNet-53 model.Compared with the classic Faster R-CNN model with the feature-extraction network VGG16,the mAP was increased by 1%,and the FPS advantage was obvious.Compared with the classic single shot multibox detector(SSD)model,both the accuracy and the running efficiency were improved.The results show that the improved YOLOv3_Litchi model had stronger robustness,higher detection accuracy,and less computational complexity for the identification of litchi in the field conditions,which should be helpful for litchi orchard precision management. 展开更多
关键词 deep learning residual network dense connection feature pyramid network
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A Character Flow Framework for Multi-Oriented Scene Text Detection 被引量:1
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作者 Wen-Jun Yang Bei-Ji Zou +1 位作者 Kai-Wen Li Shu Liu 《Journal of Computer Science & Technology》 SCIE EI CSCD 2021年第3期465-477,共13页
Scene text detection plays a significant role in various applications,such as object recognition,document management,and visual navigation.The instance segmentation based method has been mostly used in existing resear... Scene text detection plays a significant role in various applications,such as object recognition,document management,and visual navigation.The instance segmentation based method has been mostly used in existing research due to its advantages in dealing with multi-oriented texts.However,a large number of non-text pixels exist in the labels during the model training,leading to text mis-segmentation.In this paper,we propose a novel multi-oriented scene text detection framework,which includes two main modules:character instance segmentation(one instance corresponds to one character),and character flow construction(one character flow corresponds to one word).We use feature pyramid network(FPN)to predict character and non-character instances with arbitrary directions.A joint network of FPN and bidirectional long short-term memory(BLSTM)is developed to explore the context information among isolated characters,which are finally grouped into character flows.Extensive experiments are conducted on ICDAR2013,ICDAR2015,MSRA-TD500 and MLT datasets to demonstrate the effectiveness of our approach.The F-measures are 92.62%,88.02%,83.69%and 77.81%,respectively. 展开更多
关键词 multi-oriented scene text detection character instance segmentation character flow feature pyramid network(FPN) bidirectional long short-term memory(BLSTM)
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Learning Single-Shot Detector with Mask Prediction and Gate Mechanism
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作者 Jingyi Chen Haiwei Pan +2 位作者 Qianna Cui Yang Dong Shuning He 《国际计算机前沿大会会议论文集》 2020年第1期327-338,共12页
Detection efficiency plays an increasingly important role in object detection tasks.One-stage methods are widely adopted in real life because of their high efficiency especially in some real-time detection tasks such ... Detection efficiency plays an increasingly important role in object detection tasks.One-stage methods are widely adopted in real life because of their high efficiency especially in some real-time detection tasks such as face recognition and self-driving cars.RetinaMask achieves significant progress in the field of one-stage detectors by adding a semantic segmentation branch,but it has limitation in detecting multi-scale objects.To solve this problem,this paper proposes RetinaMask with Gate(RMG)model,consisting of four main modules.It develops RetinaMask with a gate mechanism,which extracts and combines features at different levels more effectively according to the size of objects.It firstly extracted multi-level features from input image by ResNet.Secondly,it constructed a fused feature pyramid through feature pyramid network,then gate mechanism was employed to adaptively enhance and integrate features at various scales with the respect to the size of object.Finally,three prediction heads were added for classification,localization and mask prediction,driving the model to learn with mask prediction.The predictions of all levels were integrated during the post-processing.The augment network shows better performance in object detection without the increase of computation cost and inference time,especially for small objects. 展开更多
关键词 Single-shot detector Feature pyramid networks Gate mechanism Mask prediction
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