An Improved YOLOv7 Model for Surface Damage Detection on Wind Turbine Blades Based on Low-Quality UAV Images

Abstract

The efficient damage detection of the wind turbine blade (WTB), the core part of the wind power, is very improtant to wind power. In this paper, an improved YOLOv7 model is designed to enhance the performance of surface damage detection on WTBs based on the low-quality unmanned aerial vehicle (UAV) images. (1) An efficient channel attention (ECA) module is imbeded, which makes the network more sensitive to damage to decrease the false detection and missing detection caused by the low-quality image. (2) A DownSampling module is introduced to retain key feature information to enhance the detection speed and accuracy which are restricted by low-quality images with large amounts of redundant information. (3) The Multiple attributes Intersection over Union (MIoU) is applied to improve the inaccurate detection location and detection size of the damage region. (4) The dynamic group convolution shuffle transformer (DGST) is developed to improve the ability to comprehensively capture the contours, textures and potential damage information. Compared with YOLOv7, YOLOv8l, YOLOv9e and YOLOv10x, this experiment’s results show that the improved YOLOv7 has the optimal detection performance synthetically considering the detection accuracy, the detection speed and the robustness.