Lightweight Detection Method of Wheelset Tread Defects Based on Improved YOLOv7

Accurate online inspection of train wheelset tread defects is challenging owing to the variety and position uncertainty of defects. This study develops an improved YOLOv7 model capable of inspecting various wheelset tread defects with high accuracy and low computation complexity. This model comprises GSConv, a small target enhancement (STE) module, and StyleGAN3. GSConv significantly reduces the model volume while maintaining the feature expression ability, achieving a lightweight structure. The STE module enhances the fusion of shallow features and distribution of attention weights, significantly improving the sensitivity to small-sized defects and positioning robustness. StyleGAN3 enhances small samples by addressing inhomogeneity, thereby generating high-quality defect samples; it overcomes the limitations of traditional amplification methods regarding texture authenticity and morphological diversity, systematically improving the model’s generalization ability under sample scarcity conditions. The model achieves 1.6%, 10.7%, 48.63% and 37.97% higher mean average precision values than YOLOv7, YOLOv5, SSD, and Faster R-CNN, respectively, and the model parameter size is reduced by 73.91, 94.69, 122.11, and 154.91 MB, respectively. Hence, the proposed YOLOv7-STE model outperforms traditional models. Moreover, it demonstrates satisfactory performance in detecting small target defects in different samples, highlighting its potential applicability in online wheel tread defect inspection.