CHBS-YOLO: a multiscale feature enhancement detection model for irregular cracks in wind turbine blade

Wind turbine blade crack detection is a critical measure for ensuring the stable operation of wind turbines. Facing the challenges of complex crack shape, irregular crack characteristics and insufficient detection accuracy, this study proposes a multi-scale feature enhancement detection model for irregular cracks of wind turbine blade based on YOLOv5, named CHBS-YOLO. To capture the local edge and global morphology of irregular blade cracks, a scheme integrating the C3TR module and the Haar wavelet transform is proposed, which enhances the focus on the crack locations and feature information of irregular cracks. On this basis, an embedding scheme integrating the Bidirectional Feature Pyramid Network (BiFPN) and the Shuffle Attention (SA) mechanism is designed to further capture crack features. By fusing multi-scale crack feature information, the proposed scheme enhances the detection model’s focus on crack features. Experimental results demonstrate that on the wind turbine blade crack dataset, the mean Average Precision (mAP) of CHBS-YOLO reaches 81.74%, outperforming classic models such as EfficientDet, SSD, Faster R-CNN, and YOLOv5s by 17.05%, 6.75%, 21.44%, and 1.47%, respectively. These results validate the effectiveness of the detection model, suggesting its applicability to the detection of irregular cracks in wind turbine blades.