Research on the application of lightweight deep learning model in automatic identification of electricity-related public safety hazards in distribution lines

Abstract

In view of the current problems of low speed, high network complexity, and difficulty in accurately detecting small target defects in insulator defect detection methods, this study proposes a lightweight insulator defect detection model. First, by enhancing both the convolutional block (CBL) and the efficient long-range aggregation network (ELAN-S) within the feature extraction network, the extraction capability for defect features is significantly augmented. Secondly, the CA-Tiny Spatial Pyramid Pooling (SPP) module was crafted by seamlessly integrating the coordinate attention mechanism with Tiny SPP, enabling the model to prioritize insulator defect characteristics and thereby enhancing defect detection accuracy. Finally, utilizing the positioning loss function WIoUv3 loss for loss calculation, a smaller gradient gain is assigned to low-quality anchor boxes, minimizing harmful gradients and enhancing the model’s positioning performance. Experimental outcomes demonstrate that the enhanced YOLOv7-tiny model excels in rapid and precise defect detection. In comparison to the original YOLOv7-tiny model, the proposed version is well-suited for edge device deployment, enabling real-time insulator defect detection.