Segmentation Head Networks with Harnessing Self-Attention and Transformer for Insulator Surface Defect Detection

Abstract

Current methodologies for insulator defect detection are hindered by limitations in real-world applicability, spatial constraints, high computational demand, and segmentation challenges. Addressing these shortcomings, this paper presents a robust fast detection algorithm combined segmentation head networks with harnessing self-attention and transformer (HST-Net), which is based on the You Only Look Once (YOLO) v5 to recognize and assess the extent and types of damage on the insulator surface. Firstly, the original backbone network is replaced by the transformer cross-stage partial (Transformer-CSP) networks to enrich the network’s ability by capturing information across different depths of network feature maps. Secondly, an insulator defect segmentation head network is presented to handle the segmentation of defect areas such as insulator losses and flashovers. It facilitates instance-level mask prediction for each insulator object, significantly reducing the influence of intricate backgrounds. Finally, comparative experiment results show that the positioning accuracy and defect segmentation accuracy of the proposed both surpass that of other popular models. It can be concluded that the proposed model not only satisfies the requirements for balance between accuracy and speed in power facility inspection, but also provides fresh perspectives for research in other defect detection domains.