Improving insulator fault detection with effective-YOLOv7 network

Abstract

Ensuring reliable and steady operation of power equipment is paramount to safeguard the livelihoods and labor of the populace. However, traditional detection techniques face obstacles when adapting to the intricate background of transmission lines, leading to innumerable incorrect and missed detections. To resolve these issues, an improved YOLOv7 transmission line insulator defect recognition algorithm has been introduced. An anchor frame, matching the size of the transmission line fault, has been constructed using K-means++, followed by a convolutional block attention module to enhance the ability to extract features. Next, the wise-IoU loss function has been incorporated, providing a gradient gain allocation strategy, thereby enhancing the positioning performance and speed of the model. Finally, the SiLU activation function has been replaced with meta-ACON to enhance the adaptive feature capability of the YOLOv7 network. Experimental results have shown that the proposed method has an average accuracy (mAP) of 91.8%, and the detection accuracy for transmission lines can be improved to 98.8%. This model resolves the persisting issues of erroneous and missing detections by addressing the traditional technical difficulties of detecting complex backgrounds and defects with insufficient accuracy.