Performance Evaluation of a Visual Defects Detection System for Railways Monitoring

SNCF Réseau introduces a novel multi-modal embedded monitoring system, addressing challenges in railway infrastructure maintenance. The design incorporates visual, inertial, and sound sensors, enhancing adaptability, improving overall detection precision, and could reduce operational costs. This study addresses visual defects detection that can be integrated in a multi-modal monitoring system. The paper details the system’s architecture, synchronisation methods, and decision fusion process to improve the precision of limited mono-modal systems. A deep-learning visual based railway defects inspection was explored. Results show that small CNN (Yolov8 nano) can achieve similar (Yolov8 XL) high precision (mAP@0.5 ≥ 0.89) for a small number of objects (9) while improving implementation capability on embedded systems.