Multi-Scale Attention-Enhanced YOLO26 for Wind Power Equipment Surface Defect Detection

Surface defects in wind power equipment exhibit substantial variations in scale, morphology, texture, and orientation and are frequently affected by complex backgrounds, illumination changes, and surface contamination, posing challenges to reliable visual inspection. This study proposes a YOLO26-based surface defect detection framework that integrates multi-scale contextual modeling, spatial–channel collaborative attention, and directional feature recalibration. Specifically, the original SPPF module is replaced with SPPSCCAP to combine multi-scale pooling, spatial–channel reconstruction, and global contextual information. CADP-SCSA is subsequently introduced to jointly model directional spatial information and inter-channel dependencies, while ELA-B modules are embedded before the P3, P4, and P5 detection heads to preserve horizontal and vertical positional information. Experiments were conducted on a public wind power equipment dataset containing 9583 images and 22,856 annotated instances across seven defect categories. Under a unified training and testing protocol, comprehensive ablation experiments demonstrate that the effects of the three enhancement mechanisms are not simply additive. Among the evaluated configurations, the three-module Model H achieved the highest values across the four reported detection metrics, with a precision of 0.9120, a recall of 0.8860, an mAP50 of 0.9060, and an mAP50–95 of 0.6660. Compared with the baseline model, these results represent improvements of 5.57, 8.99, 8.63, and 9.44 percentage points, respectively. These findings demonstrate the importance of layer-specific feature enhancement and module coupling in multi-class defect detection and provide a practical basis for vision-based inspection and condition assessment of wind power equipment.