Advanced Deep Learning Architecture for Real‐Time Online Train Tread Segmentation and Wear Detection

Train tread wear constitutes a critical factor compromising railway operational safety. Current deep learning–based image processing approaches predominantly analyze data acquired from area‐scan cameras and fail to simultaneously address detection accuracy and processing speed, thus hindering practical deployment in industrial settings. Furthermore, research on train tread centerline extraction remains virtually unexplored, despite its potential utility as key auxiliary information for rail condition monitoring. To address these limitations, we propose a real‐time train tread wear detection system. First, the tread region segmentation is implemented using a Light‐DeepLabV3+ architecture, enhanced through backbone network replacement and attention mechanism integration. Additionally, we introduce a lightweight ASPP‐L module that reduces model complexity while preserving segmentation accuracy. Second, a camera calibration–based method extracts the tread centerline. Finally, we present an Improved‐YOLOv8l model incorporating large‐scale feature maps and attention modules, which leverages the extracted centerline information for rapid and precise wear detection. Experimental validation demonstrates that our system achieves 45 FPS with 93.1% mAP on an NVIDIA RTX 3060 GPU, exceeding real‐time railway inspection requirements while maintaining state‐of‐the‐art detection performance.