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Improved edge lightweight YOLOv4 and its application in on-site power system work

Global Energy Interconnection 2022年第2期5卷 168-180页

作者： Kexin Li Liang Qin Qiang Li Feng Zhao Zhongping Xu Kaipei Liu School of Electrical Engineering and Automation Wuhan UniverstiyWuhan430072P.R.China State Grid Information&Telecommunication Group Co. LtdBeijing102211P.R.China Beijing State Grid Information&Telecommunication Accenture Information Technology Co. Ltd.Beijing 100052P.R.China

A“cloud-edge-end”collaborative system architecture is adopted for real-time security management of power system on-site work,and mobile edge computing equipment utilizes lightweight intelligent recognition algorithms for on-site risk assessment and *** to its lightweight and fast speed,YOLOv4-Tiny is often deployed on edge computing equipment for real-time video stream detection;however,its accuracy is relatively *** study proposes an improved YOLOv4-Tiny algorithm based on attention mechanism and optimized training methods,achieving higher accuracy without compromising the ***,a convolution block attention module branch is added to the backbone network to enhance the feature extraction capability and an efficient channel attention mechanism is added in the neck network to improve feature ***,three optimized training methods:transfer learning,mosaic data augmentation,and label smoothing are used to improve the training effect of this improved ***,an edge computing equipment experimental platform equipped with an NVIDIA Jetson Xavier NX chip is established and the newly developed algorithm is tested on *** to the results,the speed of the improved YOLOv4-Tiny algorithm in detecting on-site dress code compliance datasets is 17.25 FPS,and the mean average precision(mAP)is increased from 70.89%to 85.03%.

关键词： On-site power system work YOLOv4-Tiny Convolution block attention mechanism efficient channel attention Optimized training methods.

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Deep learning-based automated grading of visual impairment in cataract patients using fundus images

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High Technology Letters 2023年第4期29卷 377-387页

作者：蒋杰伟 ZHANG Yi XIE He GONG Jiamin ZHU Shaomin WU Shanjun LI Zhongwen School of Electronic Engineering Xi’an University of Posts and TelecommunicationsXi’an 710121P.R.China School of Ophthalmology and Optometry and Eye Hospital Wenzhou Medical UniversityWenzhou 325000P.R.China Ningbo Eye Hospital Wenzhou Medical UniversityNingbo 315000P.R.China

Cataract is the leading cause of visual impairment *** scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the *** this study,a deep learning-based automated grading system of visual impairment in cataract patients is proposed using a multi-scale efficient channel attention convolutional neural network(MECA_CNN).First,the efficient channel attention mechanism is applied in the MECA_CNN to extract multi-scale features of fundus images,which can effectively focus on lesion-related ***,the asymmetric convolutional modules are embedded in the residual unit to reduce the infor-mation loss of fine-grained features in fundus *** addition,the asymmetric loss function is applied to address the problem of a higher false-negative rate and weak generalization ability caused by the imbalanced dataset.A total of 7299 fundus images derived from two clinical centers are em-ployed to develop and evaluate the MECA_CNN for identifying mild visual impairment caused by cataract(MVICC),moderate to severe visual impairment caused by cataract(MSVICC),and nor-mal *** experimental results demonstrate that the MECA_CNN provides clinically meaning-ful performance for visual impairment grading in the internal test dataset:MVICC(accuracy,sensi-tivity,and specificity;91.3%,89.9%,and 92%),MSVICC(93.2%,78.5%,and 96.7%),and normal sample(98.1%,98.0%,and 98.1%).The comparable performance in the external test dataset is achieved,further verifying the effectiveness and generalizability of the MECA_CNN *** study provides a deep learning-based practical system for the automated grading of visu-al impairment in cataract patients,facilitating the formulation of treatment strategies in a timely man-ner and improving patients’vision prognosis.

关键词： deep learning convolutional neural network(CNN) visual impairment grading fundus image efficient channel attention

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AM-PSPNet:Pyramid Scene Parsing Network Based on attentional Mechanism for Image Semantic Segmentation

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国际计算机前沿大会会议论文集 2022年第1期 425-434页

作者： Dikang Wu Jiamei Zhao Zhifang Wang Department of Electronic Engineering Heilongjiang UniversityHarbin 150080China

In this paper,AM-PSPNet is proposed for image semantic ***-PSPNet embeds the efficient channel attention(ECA)module in the feature extraction stage of the convolutional network and makes the network pay more attention to the channels with obvious classification characteristics through end-to-end *** recognize the edges of objects and small objects more effectively,AM-PSPNet proposes a deep guidance fusion(DGF)module to generate global contextual attention maps to guide the expression of shallow *** average crossover ratio of the proposed algorithm on the Pascal VOC 2012 dataset and Cityscapes dataset reaches 78.8%and 69.1%,*** the other four network models,the accuracy and average crossover ratio of AM-PSPNet are improved.

关键词： Semantic segmentation efficient channel attention Deep guide fusion

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Research on Gearbox Fault Diagnosis Method Based on ECA Improved VGG16

Research on Gearbox Fault Diagnosis Method Based on ECA Impr...

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第43届中国控制会议

作者： Haoce Gong Xiaoyin Nie Gang Xie School of Electronic Information Engineering Taiyuan University of Science and Technology

To overcome the limitations of traditional neural network training samples that require a large amount of data,the idea of transfer learning is utilized to migrate the VGG16 model within the field of image recognition to this field,and a deep transfer learning model is constructed to complete the task of wind turbine gearbox fault *** data structure is changed to transform the time-domain vibration signal into a two-dimensional time-frequency map using the discrete wavelet transform,and then the efficient channel attention(ECA) mechanism is used to realize the reduction of feature dimensionality loss while capturing the feature channel information interactions to improve the accuracy of gearbox fault ***,the image after changing the data structure is input into the deep migration learning model for training,and the degree of weight migration and the model structure are fine-tuned during the experimental process,finally,the diagnostic capability of the model for the fault diagnosis and classification task is evaluated,and the corresponding validation experiments are accomplished on the public dataset of Southeast University.

关键词： Transfer learning efficient channel attention VGG16 model fault diagnosis

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