多注意力机制结合的绝缘子缺陷检测模型

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中图分类号：TP391.4 文献标识码：A 文章编号：2096-4706（2025）09-0037-06

Abstract： In the current field of defect detection for tower insulators， there is a problem that the insulator recognition is not high in foggy weather and the defective insulator is difficult to identify correctly. Therefore， an improved YOLOv5 defect detection model is proposed. Based on the YOLOv5 algorithm， the model introduces three different Attention Mechanisms of SEnet， CBAMnet and ECAnet into the three feature layers of different sizes in the backbone network feature extraction to enhance the feature extraction ability. In addition， the Focal Loss function is introduced to strengthen the control of the weight of easy classification samples and difficult classification samples. The experimental results show that the improved YOLO detection model has an AP value of 85.67% for normal insulators， an AP value of 97.33% for defective insulators， and a mAP value of 91.50% . Compared with the traditional YOLOv5 model， it increases by 0.29% ， 0.48% ， and 0.38% respectively. At the same time， its normal regression rate and defect regression rate are increased by 5.52% and 3.95% ， respectively. The improved model can better identify normal and defective insulators， which has certain reference value for subsequent insulator related research.

Keywords： insulator defect; Attention Mechanism; loss function; Object Detection; YOLOv5 algorithm

0 引 言

电塔的绝缘子是电塔的关键部件，用来支撑输电线路，将输电线路与电塔隔离。（剩余8546字）