Insulator defect detection based on BaS-YOLOv5

Abstract

Currently, the use of deep learning technologies for detecting defects in transmission line insulators based on images obtained through unmanned aerial vehicle inspection simultaneously presents the problems of insufficient detection accuracy and speed. Therefore, this study first introduced the bidirectional feature pyramid network (BiFPN) module into YOLOv5 to achieve high detection speed as well as enable the combination of image features at different scales, enhance information representation, and allow accurate detection of insulator defect at different scales. Subsequently, the BiFPN module and simple parameter-free attention module (SimAM) were combined to improve the feature representation ability and object detection accuracy. The SimAM also enabled fusion of features at multiple scales, further improving the insulator defect detection performance. Finally, multiple experimental controls were designed to verify the effectiveness and efficiency of the proposed model. The experimental results obtained using self-made datasets show that the combined BiFPN and SimAM model (i.e., the improved BaS-YOLOv5 model) performs better than the original YOLOv5 model; the precision, recall, average precision and F1 score increased by 6.2%, 5%, 5.9%, and 6%, respectively. Therefore, BaS-YOLOv5 substantially improves detection accuracy while maintaining a high detection speed, meeting the requirements for real-time insulator defect detection.