In the process of wind turbine blade defect detection, to address the challenges of extracting fine-grained features and inaccurate positioning due to blurred defect textures and large-scale differences, this paper proposes a wind turbine blade defect detection algorithm (SASED-YOLO), which integrates a collaborative attention mechanism and multi-scale feature space pooling. First, a collaborative attention mechanism (CADP-SCSA) is designed and incorporated into the feature extraction network to minimize interference from complex backgrounds, effectively enhancing the extraction of multi-scale features within the global context, and improving localization accuracy. Second, a multi-scale feature space pooling module (SPPSCCAP) is designed to enhance the processing and fusion of fine-grained, multi-scale defect features on wind turbine blades. The C2f-SENetv2 module is employed to enhance the representation of features across different channels. Finally, an adaptive slice convolution module (FADown) is designed to effectively reduce information loss during the sampling process. Experimental analysis is performed on the self-constructed wind turbine blade dataset (WTBD818-DET). The proposed algorithm achieved a mean average precision (mAP) of 87.7%, which is 10.5% higher than YOLOv8s, and outperforms mainstream detection algorithms such as RT-DETR, YOLOv11s, and Mamba. The experimental results indicate that the algorithm maintains high performance in detecting multi-scale wind turbine blade defects.
Lv et al. (Thu,) studied this question.