计算机与现代化Issue(2)：114-120,7.DOI:10.3969/j.issn.1006-2475.2025.02.016
基于改进YOLOv5的架空线路关键部件典型缺陷识别
Identification of Typical Defects in Key Components of Overhead Lines Based on Improved YOLOv5
摘要
Abstract
The key components in overhead lines may suffer from damage,detachment,and other defects due to long-term expo-sure to the natural environment.It is difficult to detect and repair these defects manually.To address the aforementioned issues,this paper proposes an light-weighted,edge computing device suited,improved YOLOv5 based detection method.Firstly,an EMA module is added at the end of the backbone network to enhance the network's ability to capture features.Secondly,the CBS module of the neck will be replaced with GhostConv,and the C3 module of the neck will be combined with SENetV2 to make the network more lightweight while enhancing its representational ability.The experimental results demonstrate that the pro-posed method achieves a significant improvement in class-average accuracy compared to YOLOv5,while maintaining real-time detection capability with only marginal frame rate reduction.Compared with SSD and Faster R-CNN algorithms,it has certain ad-vantages in detection accuracy and speed.关键词
YOLOv5/目标检测/架空线路/EMA/GhostConv/SENetV2Key words
YOLOv5/object detection/aerial lines/EMA/GhostConv/SENetV2分类
信息技术与安全科学引用本文复制引用
王鹏,倪彬,郭壮壮,张书盛,王志,蔡润楷..基于改进YOLOv5的架空线路关键部件典型缺陷识别[J].计算机与现代化,2025,(2):114-120,7.基金项目
国网新疆电力有限公司科技项目(5230BD230003) （5230BD230003）
