Lightweight Detection Method of Wheelset-Tread Defects Based on Improved YOLOv7

Train wheelsets are key components of high-speed trains and are prone to failure during long-term operation under harsh conditions. Accurate and comprehensive online inspection of defects in wheelset treads are difficult because of their variety and position uncertainty. Accurate detection and location of small targets are highly challenging. This study developed an improved YOLOv7 model with better accuracy and lower power computation. The model was applied to the inspection of various wheelset-tread defects. GSConv was used to reduce the model volume, while a small target enhancement module addressed low pixels and difficulty in distinguishing small targets. This improved the classification accuracy for damage feature recognition. The captured images of the wheelset tread were preprocessed using StyleGAN3 to augment small samples and address non-uniformities. The experimental results showed that the proposed model exhibited a remarkably better overall performance than conventional models, particularly in terms of mean average precision. The public RSDDs dataset of small rail defects was selected to verify the robustness of the proposed algorithm. Compared with the traditional network model, the improved model proposed herein showed good performance for small target defects in different samples, highlighting its potential applicability in online inspection of wheel tread defects.