Surface defect detection of wind turbine based on lightweight YOLOv5s model

Abstract

The surface defect detection and maintenance of wind turbines affect the efficiency of wind power generation. However, traditional detection methods suffer from insufficient accuracy and large numbers of calculations. To solve this problem, we propose a surface defect detection model of wind turbines based on the lightweight YOLOv5s. Firstly, the YOLOv5s backbone network was replaced by the MobileNetv3 lightweight network for feature extraction to coordinate and balance the lightweight and accuracy relationship of the model. The neck network was realized as a weighted bi-directional feature pyramid network to enhance the model’s multi-scale adaptability and improve its fusion speed and efficiency. To adaptively adjust the feature weights and further improve the feature extraction ability, the convolution block attention module was used as the space and channel attention mechanism. Finally, the loss function was changed from border regression to the efficient intersection over union loss, which improved the accuracy of the bounding box regression. Compared with the original YOLOv5s model, the mean average precision was increased by 5.51 %, and the detection time was reduced by 10.79 frames per second, which shows that the improved model can complete the real-time target detection task on embedded devices with high accuracy and low energy consumption.