This paper proposes a detection method based on an improved YOLOv5s model for the problem of slight cracks in wind turbine blades that are difficult to detect. The method mainly includes three improvements. First, in the backbone network part, ASPP (atrous spatial pyramid pooling) is used instead of SPP (spatial pyramid pooling) to adapt to targets of different sizes and proportions. Second, SE (squeeze and excitation) attention modules are inserted into the backbone network to increase the network's sensitivity to small defects; and SIoU-Loss is used to replace the original CIoU-Loss to further improve the accuracy and training speed of the new network. Finally, a comparison experiment is conducted using a self-built dataset. Experimental results show that the mAP value of the improved YOLOv5s model is 94.29%, which is 7.03 percentage points higher than that of the YOLOv5s model, and its detection accuracy has advantages over other mainstream models. The detection speed is 42.78f/s. This method has good performance and effect in detecting defects inside wind turbine blades.

YOLOv5s model  /  turbine blade  /  defect detection