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A Model for Helmet-Wearing Detection of Non-Motor Drivers Based on YOLOv5s

Computers, Materials & Continua 2023年第6期75卷 5321-5336页

作者： Hongyu Lin Feng Jiang Yu Jiang Huiyin Luo Jian Yao Jiaxin Liu College of Computer&Information Engineering Central South University of Forestry and TechnologyChangsha410004China

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.

关键词： Helmet-wearing detection dilated convolution feature pyramid network feature fusion

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SPM-IS: An auto-algorithm to acquire a mature soybean phenotype based on instance segmentation

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The Crop Journal 2022年第5期10卷 1412-1423页

作者： Shuai Li Zhuangzhuang Yan Yixin Guo Xiaoyan Su Yangyang Cao Bofeng Jiang Fei Yang Zhanguo Zhang Dawei Xin Qingshan Chen Rongsheng Zhu College of Engineering Northeast Agricultural UniversityHarbin 150030HeilongjiangChina College of Arts and Sciences Northeast Agricultural UniversityHarbin 150030HeilongjiangChina College of Agriculture Northeast Agricultural UniversityHarbin 150030HeilongjiangChina

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.

关键词： Soybean feature pyramid network PCA Instance segmentation Deep learning

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Parallel channel and position attention-guided feature pyramid for pig face posture detection

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International Journal of Agricultural and Biological Engineering 2022年第6期15卷 222-234页

作者： Zhiwei Hu Hongwen Yan Tiantian Lou College of Information Science and Engineer Shanxi Agricultural UniversityJinzhong 030801ShanxiChina School of Computer and Information Technology(School of Big Data) Shanxi UniversityTaiyuan 03006China College of Agricultural Economics and Management Shanxi Agricultural UniversityJinzhong 030801ShanxiChina

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

关键词： objection detection attention mechanism feature pyramid network face posture detection pig

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Litchi detection in the field using an improved YOLOv3 model

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International Journal of Agricultural and Biological Engineering 2022年第2期15卷 211-220页

作者： Hongxing Peng Chao Xue Yuanyuan Shao Keyin Chen Huanai Liu Juntao Xiong Hu Chen Zongmei Gao Zhengang Yang College of Mathematics and Informatics South China Agricultural UniversityGuangzhou 510642China College of Mechanical and electronic engineering Shandong Agricultural UniversityTai’an 271018ShandongChina School of Electronic and Information Engineering Jiaying UniversityMeizhou 514015GuangdongChina School of Chemistry and Chemical Engineering South China Technology of UniversityGuangzhou 510641China Center for Precision and Automated Agricultural Systems Department of Biological Systems EngineeringWashington State UniversityProsserWA 99350USA

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

关键词： deep learning residual network dense connection feature pyramid network

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A Traffic Video Completion Model Based on Generative Adversarial networks

A Traffic Video Completion Model Based on Generative Adversa...

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2021中国智能自动化大会(CIAC2021)

作者： Lan Wu Han Wang Tian Gao Binquan Li Fanshi Kong College of Electrical Engineering Henan University of Technology Zhengzhou Railway Vocational and Technical College

Aiming at the problem of missing traffic video frames, this paper proposes a complementary model of generative adversarial networks. The model uses the feature pyramid network(FPN) to obtain feature maps of multiple scales on the input video frame. By fusing feature maps of different scales, it can better integrate the semantic information on the frame. The local patch discriminator added to the discriminator model effectively ensures the accuracy and continuity of the completed frame. Experimental results on Caltech pedestrian dataset and KITTI dataset show the good performance of the proposed model.

关键词： Generative adversarial networks feature pyramid network Semantic information

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