三峡大学学报（自然科学版）2025，Vol.47Issue(3)：105-112,8.DOI:10.13393/j.cnki.issn.1672-948X.2025.03.015
基于改进YOLOv5s的输电线路红外图像多目标检测
Multi-Target Detection of Infrared Images in Transmission Lines Based on Improved YOLOv5s
摘要
Abstract
To improve the reliability and accuracy of infrared image detection in transmission lines,a method YOLOv5s ECW based on the improved YOLOv5s for the detection and fault recognition in transmission line is proposed in this paper from the perspectives of practicality and multi-target detection.Firstly,an efficient multi-scale attention mechanism for cross spatial learning is added to the Backbone section to enhance the model's ability in order to extract the features and perform the multi-scale fusion.Furthermore,the computational overhead is reduced;Secondly,a context enhancement module is introduced in the Neck section to reduce the information conflicts and improve the detection accuracy of small and distant targets;Finally,the loss function is replaced with Wise IoU,which focused the model on anchor boxes of ordinary quality and improved the detection performance.The experiments and tests show that the proposed method YOLOv5s-ECW has improved the average accuracy by 3.9%,the accuracy by 4.0%,the recall by 4.5%compared with that of the original YOLOv5s.Moreover,its detection ability for five types of power equipment and possible fault points has been enhanced in various degrees.The method is more practical.关键词
YOLOv5s/红外图像/多尺度融合/小目标检测Key words
YOLOv5s/infrared images/multi-scale fusion/small object detection分类
计算机与自动化引用本文复制引用
杨春萍,刘凯波,刘慕然..基于改进YOLOv5s的输电线路红外图像多目标检测[J].三峡大学学报（自然科学版）,2025,47(3):105-112,8.基金项目
国网新疆电力有限公司科技项目(5230BD230003) （5230BD230003）
