机械与电子2025，Vol.43Issue(12)：10-17,23,9.
面向绝缘子微小缺陷的YOLOv8s的改进算法研究
Research on Improved YOLOv8s Algorithm for Micro-defect Detection in Insulators
摘要
Abstract
Detecting small-scale insulator surface defects remains challenging due to weak feature rep-resentation and complex background interference.To address this issue,this study proposes YOLOv8-CEDA,an enhanced lightweight object detection framework based on YOLOv8s,integrating fine-grained feature enhancement.First,a Ghost and Coordinate Attention(GCA)module is designed,combining the parameter efficiency of Ghost convolution with the spatial modeling capability of Coordinate Attention to improve small defect localization while reducing computational overhead.Second,an efficient channel atten-tion(ECA)mechanism is incorporated into the detection head to adaptively amplify discriminative fea-tures,strengthening the response to subtle defect patterns.Additionally,DySample,a dynamic upsampling operator,replaces conventional interpolation to preserve structural details and enhance edge recovery dur-ing multi-scale feature fusion.Extensive experiments demonstrate that YOLOv8-CEDA achieves superi-or detection accuracy over baseline models while maintaining computational efficiency,particularly in small defect detection.The proposed method exhibits strong potential for real-world industrial applications.关键词
绝缘子缺陷/YOLOv8s/注意力机制/小目标检测Key words
insulator defects/YOLOv8s/attention mechanism/small object detection分类
信息技术与安全科学引用本文复制引用
潘学华,梁炜皓,李金瑾,张震,龚宇平,尹显贵,刘益畅..面向绝缘子微小缺陷的YOLOv8s的改进算法研究[J].机械与电子,2025,43(12):10-17,23,9.基金项目
广西电网公司科技项目资助(GXKJXM20230003) （GXKJXM20230003）
