Abstract: An improved YOLOv4 wind turbine blade damage detection method is proposed to solve the problem that on-line detection of embedded equipment is difficult due to high complexity of deep convolution neural network model. Firstly, MobileNetv3 network is used to replace CSPDknet53 backbone feature extraction network in YOLOv4 for feature extraction, and feature extraction is enhanced by feature layer of the same shape. Secondly, an attention mechanism ECA is added to the enhanced feature extraction network, and the loss function of YOLOv4 boundary frame and the loss function of classification are optimized. Finally, the improved algorithm is compared with other detection algorithms. The result shows that the detection speed of the improved YOLOv4 algorithm can reach 0.018seconds per sheet and the detection accuracy reaches 95.7%. Through improving the YOLOv4 network, the lightweight model can meet the requirement of embedded equipment to detect wind turbine blade damage on the premise of accurate detection.