Abstract
Defects of solar panels can easily cause electrical accidents. The YOLO v5 algorithm is improved to make up for the low detection efficiency of the traditional defect detection methods. Firstly, it is improved on the basis of coordinate attention to obtain a LCA attention mechanism with a larger target range, which can enhance the sensing range of target features in addition to fully capturing feature information; secondly, the weighted bidirectional feature pyramid is used to balance the feature information with excessive pixel differences by assigning different weights, which is more conducive to multi-scale Fast fusion of features; finally, the usual coupled head of YOLO series is replaced with decoupled head, so that the task branch can be performed more accurately and the detection accuracy can be improved. The results of comparative experiments on the solar panel defect detection data set show that after the improvement of the algorithm, the overall precision is increased by 1.5%, the recall rate is increased by 2.4%, and the mAP is up to 95.5%, which is 2.5% higher than that before the improvement. It can more accurately determine whether there are defects, standardize the quality of solar panels, and ensure electrical safety.