An Improved Solov2 Based on Attention Mechanism and Weighted Loss Function for Electrical Equipment Instance Segmentation

作     者：Junpeng Wu Zhenpeng Liu Xingfan Jiang Xinguang Tao Ye Zhang 

作者机构：Key Laboratory of Modern Power System Simulation and Control&Renewable Energy TechnologyMinistry of EducationNortheast Electric Power UniversityJilin132012China School of Electrical EngineeringNortheast Electric Power UniversityJilin132012China Baishan Power Supply CompanyState Grid Jilin Electric Power CoLtd.Jilin134300China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第78卷第1期

页      面：677-694页

核心收录：

学科分类：04[教育学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Jilin Science and Technology Development Plan Project(No.20200403075SF) Doctoral Research Start-Up Fund of Northeast Electric Power University(No.BSJXM-2018202). 

主　　题：Deep learning electrical equipment attention mechanism weighted loss function 

摘      要：The current existing problem of deep learning framework for the detection and segmentation of electrical equipment is dominantly related to low precision.Because of the reliable,safe and easy-to-operate technology provided by deep learning-based video surveillance for unmanned inspection of electrical equipment,this paper uses the bottleneck attention module(BAM)attention mechanism to improve the Solov2 model and proposes a new electrical equipment segmentation mode.Firstly,the BAM attention mechanism is integrated into the feature extraction network to adaptively learn the correlation between feature channels,thereby improving the expression ability of the feature map;secondly,the weighted sum of CrossEntropy Loss and Dice loss is designed as the mask loss to improve the segmentation accuracy and robustness of the model;finally,the non-maximal suppression(NMS)algorithm to better handle the overlap problem in instance segmentation.Experimental results show that the proposed method achieves an average segmentation accuracy of mAP of 80.4% on three types of electrical equipment datasets,including transformers,insulators and voltage transformers,which improve the detection accuracy by more than 5.7% compared with the original Solov2 model.The segmentation model proposed can provide a focusing technical means for the intelligent management of power systems.