吉林大学学报（信息科学版）2026，Vol.44Issue(1)：61-70,10.
新型YOLOv3-Tiny在绝缘子故障检测中的应用
Application of New YOLOv3-Tiny in Insulator Fault Detection
摘要
Abstract
Insulator faults in transmission lines directly threaten power grid stability.To accurately identify insulator faults,an insulator fault diagnosis method based on an improved YOLOv3(You Only Look Once v3)-Tiny framework is proposed.Firstly,a channel-spatial attention mechanism is integrated into the feature extraction network to enhance critical feature capture capabilities.Subsequently,a CSP-RFB(Cross Stage Partial-Receptive Field Block)module is designed to improve small-target detection performance while reducing computational complexity.Finally,a novel loss function is adopted to optimize localization accuracy.Experimental results demonstrate that the enhanced YOLOv3-Tiny algorithm achieves up to 97.4％MAP(Mean Average Precision)in insulator fault detection,significantly outperforming the original YOLOv3-Tiny model.关键词
深度学习/缺陷检测/绝缘子故障诊断Key words
deep learning/defect detection/insulator fault diagnosis分类
信息技术与安全科学引用本文复制引用
王炳北,郑辉,孙德罡..新型YOLOv3-Tiny在绝缘子故障检测中的应用[J].吉林大学学报（信息科学版）,2026,44(1):61-70,10.基金项目
海南省自然科学基金资助项目(623MS071) （623MS071）
