Improving Detection and Positioning of Insulators in YOLO v7.

This paper aims to address the problems of low accuracy and high leakage rate due to the influence of different insulator sizes and background interference in the target detection task of power systems. Firstly, a convolutional block attention module (CBAM) is added to the YOLO v7 backbone network to make the network model pay more attention to the insulator features from both channel and space aspects and reduce the leakage rate in insulator detection. Secondly, a concentrated feature pyramid (CFP) is added to the deeper layer of the network model to allow the information exchange and aggregation of feature maps at different scales, thus obtaining more comprehensive insulator features and improving insulator detection accuracy. Finally, the k-means algorithm is used to cluster the preselected frames to obtain the most suitable insulator preselected frame size. The experimental results show that the improved YOLO v7 network model has a detection mAP (mean average precision) of 96.2%, a precision of 90.8%, and a recall of 93.8%. The improved method in this paper has a wide application prospect in the insulator detection of power systems. [ABSTRACT FROM AUTHOR]