Algorithm for detecting surface defects in wind turbines based on a lightweight YOLO model.

Improving wind power generation efficiency and lowering maintenance and operational costs are possible through the early and efficient diagnosis and repair of surface defects in wind turbines. To solve the lightweight deployment difficulty and insufficient accuracy issues of the traditional detection methods, this paper proposes a high-precision PC-EMA block based on YOLOv8 using partial convolution (PConv) combined with an efficient multiscale attention (EMA) channel attention mechanism, which replaces the bottleneck layer of the YOLOv8 backbone network to improve the extraction of target feature information from each layer of the network. In the feature fusion phase, GSConv, which can retain more channel information, is introduced to balance the model's complexity and accuracy. Finally, by merging two branches and designing the PConv head with a low-latency PConv rather than a regular convolution, we are able to effectively reduce the complexity of the model while maintaining accuracy in the detection head. We use the WIoUv3 as the regression loss for the improved model, which improves the average accuracy by 5.07% and compresses the model size by 32.5% compared to the original YOLOv8 model. Deployed on Jetson Nano, the FPS increased by 11 frames/s after a TensorRT acceleration.