Insulator defect detection algorithm based on attention mechanism

With the rapid development of deep learning, using object detection algorithms to detect defects in aerial insulator images has become the main way of insulator inspection. In response to the problems of low detection accuracy of small targets, weak representation ability of feature maps, and limited extraction of key information in traditional object detection algorithms, this paper introduces the ECA attention module into the YOLOv51 model, which can effectively enhance and suppress a large amount of feature information at the channel level of the feature map, improve the network's attention to defect areas, and fuse shallow and deep information of insulators to avoid the loss of feature information and reduce the incidence of missed detections. The improved model has improved the accuracy of insulator defect detection, especially in identifying small target defects.