YOLOv5 insulator defect detection method based on multi-head self-attention

Abstract

Insulators are important components of transmission lines. Aiming at the problems of small detection targets and complex detection backgrounds in the images of insulators taken by drones in power inspections, this paper proposes an improved BM-YOLOv5 insulator defect detection method based on the YOLOv5 network. The multi-head self-attention structure is introduced to improve the performance of the model to capture long-distance dependencies, so that it can pay attention to global information; the original feature pyramid structure is replaced with a weighted bidirectional feature pyramid structure, and additional weights are added to features of different scales. Make full use of features between different scales. Based on the newly constructed insulator defect data set with complex background and small targets, the method is compared with the commonly used insulator defect detection models YOLOv5, YOLOv3, Faster-RCNN, and SSD. Compared with YOLOv5, the average accuracy has increased by 5.1%.