宁夏电力Issue(2)：75-82,8.DOI:10.3969/j.issn.1672-3643.2024.02.013
基于最优传输分配的改进YOLOv5s的变电站屋面缺陷检测算法
An improved YOLOv5s algorithm for detecting substation rooftop defects based on optimal transport assignment
摘要
Abstract
The thermal insulation,waterproofing,isolation,vapor barrier,and leveling layers are critical structural and functional components of substation rooftops.Defects on these layers can significantly impact the performance,lifespan,and personnel safety of substations.This study proposes an improved target detection solution for these surface defects using a you only look once,YOLOv5s algorithm enhanced with the optimal transport assignment(OTA).The OTA algo-rithm refines label assignment,providing a more accurate match than traditional threshold methods and balancing the learning between positive and negative samples.Experimental results demonstrate that the OTA-optimized YOLOv5s algorithm can comprehensively utilize image information and learn geometric features,thereby reducing localization loss,object loss,and classification loss.Furthermore,the OTA enhances precision,recall,and mean average precision(MAP),indicating improved predictive accuracy,integrity,and overall performance of the model.Therefore,the application of the YOLOv5s algorithm combined with OTA optimization for rooftop defect detection in substations holds significant practi-cal value.关键词
最优传输分配/变电站屋面缺陷/YOLOv5s目标检测Key words
optimal transport assignment/substation rooftop defects/YOLOv5s object detection分类
信息技术与安全科学引用本文复制引用
徐波,张晓晨..基于最优传输分配的改进YOLOv5s的变电站屋面缺陷检测算法[J].宁夏电力,2024,(2):75-82,8.基金项目
国网宁夏建设分公司2023年群众性科技创新项目(5229JS230002) （5229JS230002）
