无线电工程2025，Vol.55Issue(5)：938-948,11.DOI:10.3969/j.issn.1003-3106.2025.05.005
一种基于改进的YOLOv8的高压输电线路绝缘子缺陷检测方法
A High-voltage Transmission Line Insulator Defect Detection Method Based on Improved YOLOv8
摘要
Abstract
To address the issues of false detection,missed detection and low detection accuracy in existing insulator defect detection algorithms,a high-voltage transmission line insulator defect detection method based on improved YOLOv8 model is proposed for high-accuracy detection.First,a deformable attention backbone network is designed using a Deformable Convolutional Neural Network(DCNN)combined with a Global Attention Mechanism(GAM),reducing the loss of effective object features during feature extraction.Next,an improved Spatial Pyramid Pooling Fast Feature Fusion(SPFF)module is proposed based on the Convolutional Block Attention Module(CBAM),combining with the Efficient Channel Attention(ECA)mechanism to expand the model's receptive field and preserve more types of defect features,thereby improving the detection accuracy.Additionally,the Stable Intersection over Union(SIoU)loss function is introduced to speed up the model's convergence and enhance small object defect detection capabilities.Finally,a dataset containing four types of insulator defects,i.e.Normal,Defect,Broke,Flashover,is constructed.Experimental results show that the improved YOLOv8 model achieves an mean Average Precision(mAP)of 95.84％,which is 5.58％higher than the original YOLOv8,and outperforms other models in terms of AP values across all types of insulators.The improved YOLOv8 model also shows significant improvement in detecting small object defects compared to the original algorithm,further validating its feasibility and effectiveness for insulator defect detection.关键词
绝缘子缺陷检测/深度学习/可变形注意力骨干网络/改进的空间金字塔池化快速特征融合模块/小目标缺陷检测Key words
insulator defect detection/deep learning/deformable attention backbone network/improved SPFF module/small object defect detection分类
计算机与自动化引用本文复制引用
赵永祥,张国庆,罗巍,李晓亮..一种基于改进的YOLOv8的高压输电线路绝缘子缺陷检测方法[J].无线电工程,2025,55(5):938-948,11.基金项目
博士启动基金(BKY-2021-26) （BKY-2021-26）
南京航空航天大学空间光电探测与感知工业和信息化部重点实验室开放课题资助(NJ2024027-8) （NJ2024027-8）
中央高校基本科研业务费资助(NJ2024027)Project supported by Doctoral Research Startup Fund(BKY-2021-26) （NJ2024027）
Supported by Open Project Funds for the Key Laboratory of Space Photoelectric Detection and Perception(Nanjing University of Aeronautics and Astronautics),Ministry of Industry and Information Technology(NJ2024027-8) （Nanjing University of Aeronautics and Astronautics）
Supported by the Fundamental Research Funds for the Central Universities(NJ2024027) （NJ2024027）
