An automatic defect detection system based on deep learning for fasteners in the power system

Fasteners on transmission lines are widely used in various power connection components, and the loosening and missing of fastener cotter pins can trigger problems such as falling off of components, which has a huge potential safety hazard. It is necessary to propose an automatic fastener defect detection method to ensure the stable operation of the transmission line. In this paper, a three-stage cascade system for automatic detection of fastener defects is constructed in a coarse-to-fine mode, including fastener location network (FLN), feature refinement network (FRN), and defect diagnosis network (DDN). First, the proposed FLN improves the YOLOV4 model based on a blurred module to locate fasteners. Then, the proposed FRN is applied to extract the semantic information of the fasteners and refine the features. Finally, the proposed DDN is used to classify the cotter pin defects. To verify the adaptability and accuracy of the method, a considerable amount of experiments and analyses have been performed, and the results show that the proposed detection method reaches an accuracy of 98.4% and testing speed of 0.64s, achieving state-of-the-art (SOTA) performance.