Component Detection of Overhead Transmission Line Based on CBAM-Efficient-YOLOv5

Abstract

In view of the low efficiency of the staff who need to detect the insulator, grading ring, spacer and other components with their naked eyes when inspecting the transmission line manually, a CBAM Efficient YOLOv5 algorithm is proposed to realize the automatic detection of the above components. The algorithm improves the lightweight of YOLOv5, uses EfficientNet lightweight network to replace the original feature extraction network, and uses CBAM (Convolutional Block Attention Module) with both channel and spatial attention to replace SE (Sequence and Exception) attention module. Finally, a lightweight network model is obtained, in which the model size is reduced by 45.8%, the average precision is 95.2%, and the detection precision of all targets is improved to more than 90%. On the premise of meeting the precision requirements, it is easier to deploy in embedded equipment such as unmanned aerial vehicle, so as to improve the work efficiency of line patrol.