Wind turbine blade surface defect detection model based on improved you only look once version 10 small and integrated compression

Abstract

This paper introduces a new model built on the You Only Look Once version 10 small (YOLOv10s) baseline to address challenges in wind turbine blade surface defect detection, including low accuracy due to complex backgrounds, small targets, and dense defects, as well as issues of model over-parameterization and high memory consumption. Several improvements are incorporated to enhance detection accuracy: (1) the original Spatial Pyramid Pooling Fast (SPPF) module is replaced with a lightweight Contextual Augmentation Module (CAM-DW) to improve feature fusion, (2) Efficient Multi-Scale Attention (EMA) substitutes Partial Self-Attention (PSA) for better feature extraction, and (3) the Wise-Intersection over Union version 1 (WIoU-V1) loss function optimizes detection performance for high-density defect samples. To tackle the problem of excessive parameters and memory usage, an integrated compression method is proposed, which combines isomorphic pruning to reduce parameters and memory usage with channel-wise knowledge distillation to recover accuracy lost during pruning, thus striking a balance between model complexity and performance. Experimental results show that the proposed model reduces parameters by 69.7 % and memory usage by 68.1 % compared to the baseline. Its mean Average Precision mAP50 (prediction confidence threshold: 0.5) and mAP50-95 (prediction confidence thresholds: 0.5–0.95) improved by 3.3 % and 3.8 %, respectively, while detection speed increased by 46.7 Frames Per Second (FPS). These results demonstrate that the proposed model outperforms mainstream models, significantly enhancing the accuracy and efficiency of wind turbine blade surface defect detection, and providing crucial support for intelligent wind power equipment operation and maintenance.