Railway fastener defect detection using RFD-DETR: A lightweight real-time transformer-based approach.

ABSTRACT: Railway fasteners play a crucial role in ensuring track stability and safety; however, manual inspection is both inefficient and prone to errors. Deep learning models, such as YOLO, are widely utilized for defect detection. However, these methods demand comparatively larger model sizes, greater computational power, and more storage capacity, yet their defect feature extraction remains inadequate. To overcome these challenges, this research introduces RFD-DETR, an enhanced detection transformer model specifically optimized for real-time rail fastener defect identification. The model incorporates three distinct modules to facilitate multi-scale feature extraction, enhance model efficiency, and improve defect detection. Firstly, a wavelet transform convolution module (WTConv) is employed, whi

SUBMITTER: Zhou H 

PROVIDER: S-EPMC12599954 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature