Defect detection of the surface of wind turbine blades combining attention mechanism

The proposed work introduces a novel, lightweight feature fusion network model based on the attention mechanism to address the issues of high time consumption and poor effectiveness in wind turbine blade surface defect detection. A bidirectional feature fusion network enhances the YOLOX network. The model employs an attention mechanism module to learn channel and spatial feature information. A classification loss function with an attenuation factor is designed to tackle the unbalanced distribution of learning weights for multiple samples in the blade surface defect detection task. Additionally, to mitigate recognition loss caused by target occlusion or overlap, the Soft-NMS method is utilized to eliminate redundant detection boxes. Sample imbalance is addressed by creating new samples using a geometric transformation-based multi-sample fusion data enhancement method and unsupervised learning MFF-GAN image editing techniques. A feature fusion network comparison test, classification loss function comparison test, and attention mechanism module position ablation test were completed using the blade surface defect dataset. The results indicate that the blade surface defect detection model incorporating the attention mechanism can effectively identify five types of defects with an average detection accuracy of mAP-0.5, achieving approximately 95.03 % and a detection frame rate of about 54.56 frame·s−1. Compared with the YOLOX-s network, YOLOv7-tiny network, and YOLOv8-s network, this model can improve the recognition rate and shorten the detection time, achieving high precision and rapid identification and localization of blade surface defects.