Multi-Scale CNN-Transformer Hybrid Network for Rail Fastener Defect Detection
Jin He, Wei Wang, Fengmao Lv, Haonan Luo, Gexiang Zhang, Zhenghua Chen
Abstract
Defect detection in rail fasteners is crucial for train safety, as defective fasteners can cause derailments and severe safety incidents. However, Existing algorithms often struggle in various real-world scenarios due to challenges such as obscured fasteners, motion blur in images, varying camera angles, and fasteners submerged in water. To address these challenges, we propose a Multi-scale CNN-Transformer Hybrid Network for Rail Fastener Defect Detection (MCHNet-RF2D), specifically designed to identify fastener defects in complex environments. Our approach constructs an efficient CNN block and a multi-scale Vision Transformer block to alternately extract local detail features and global semantic features of the fasteners. These features are seamlessly integrated through multi-scale fusion to enhance defect recognition robustness. By combining comprehensive global recognition with detailed local defect detection, MCHNet-RF2D outperforms existing CNN-Transformer hybrid networks by 2.8% and surpasses current fastener defect detection algorithms by 2.9%. In practical deployment on over 40 trains, our model successfully detected more than 2,000 fastener defects, demonstrating its effectiveness in diverse and challenging conditions.