基于改进YOLOX的变电站仪表外观缺陷检测算法  

Appearance defect detection algorithm of substation instrument based on improved YOLOX

作　　者：赵振兵[1,2,3] 马迪雅 石颖 李刚[4] ZHAO Zhen-bing;MA Di-ya;SHI Ying;Li Gang(Department of Electronic and Communication Engineering,North China Electric Power University,Baoding Hebei 071003,China;Hebei Key Laboratory of Power Internet of Things Technology,North China Electric Power University,Baoding Hebei 071003,China;Engineering Research Center of Intelligent Computing for Complex Energy Systems,Ministry of Education,North China Electric Power University,Baoding Hebei 071003,China;School of Control and Computer Engineering,North China Electric Power University,Baoding Hebei 071003,China)

机构地区：[1]华北电力大学电子与通信工程系,河北保定071003 [2]华北电力大学河北省电力物联网技术重点实验室,河北保定071003 [3]华北电力大学复杂能源系统智能计算教育部工程研究中心,河北保定071003 [4]华北电力大学控制与计算机工程学院,河北保定071003

出　　处：《图学学报》2023年第5期937-946,共10页Journal of Graphics

基　　金：国家自然科学基金项目(61871182,U21A20486);河北省自然科学基金项目(F2020502009,F2021502008,F2021502013)。

摘　　要：针对变电站仪表外观缺陷检测任务中存在的缺陷分布广、缺陷外观特征变化多样与特征提取困难问题,提出了基于改进YOLOX的变电站仪表外观缺陷检测算法。通过对比样本策略寻找到各类外观缺陷中有判别力的特征并对其进行深入挖掘;接着在网络的骨干部分加入全局上下文信息模块(GC-Model),增强模型对于外观缺陷特征的学习能力,提高网络性能;最后在预测部分使用SIoU损失函数,充分考虑方向框的角度问题对于模型优化的影响,从而提高对变电站仪表外观缺陷检测的准确率,减少漏检、误检情况发生。在实验中将选取4种典型的变电站仪表外观缺陷类型作为实验对象,通过实验结果分析,相较基线模型,平均精确率(mAP)提升了6.2%,可以有效提升变电站仪表外观缺陷的检测效果,为无人变电站的智能监测提供了有利条件。In response to the wide distribution of defects,the variety of appearance defect features,and the challenges in extracting features during the substation instrument appearance defect detection task,the appearance defect detection algorithm of substation instrument based on improved YOLOX was proposed.Through the contrast sample strategy,the discriminative features of various appearance defects could be identified and extracted in depth.Additionally,the Global Context Feature Module was added to the network backbone to enhance the learning ability of the model for appearance defect features and improve the network performance.Finally,the SIoU loss function was integrated in the prediction part to fully consider the influence of the direction frame’s angle on model optimization,thereby improving the accuracy of detecting appearance defects in substation instrument and reducing the occurrence of missed detection and false detection.In the experiment,four typical types of appearance defects of substation instruments were selected as the experimental objects.The analysis of experimental results showed an average accuracy rate increase of 6.2%compared with the baseline model.This substantial improvement effectively enhanced the detection effect for appearance defects in substation instruments,thus creating favorable conditions for intelligent monitoring of unmanned substations.

关 键 词：变电站仪表 外观缺陷检测 YOLOX 对比样本策略 上下文信息模块 SIoU 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]