Autonomous UAV-based intelligent inspection framework for rail transit infrastructure: A review of technological developments

To address the limitations of traditional manual inspection of rail transit infrastructure, such as low efficiency, safety risks, and the dependence of existing rail-mounted detection equipment on maintenance time gaps, which leads to blind spots and limited coverage, this study develops an integrated“End-Edge-Cloud-Surveillance”framework for autonomous unmanned aerial vehicle (UAV) based intelligent inspection in rail transit. At the“End”layer, multi-source perception combining visible light, infrared, and LiDAR, together with visual-inertial state estimation, enables autonomous perception and task-level navigation. At the“Edge”layer, beyond-visual-line-of-sight (BVLOS) communication and secure, efficient data transmission mechanisms are established, alongside lightweight onboard inference for real-time defect and risk detection. At the“Cloud”and“Surveillance”layers, cross-scenario and multi-target inspection applications are conducted with global data analytics, while a low-altitude surveillance system integrating cooperative and non-cooperative surveillance is established to ensure regulatory compliance and operational safety throughout the entire process. The results demonstrate that this work systematically identifies the unique challenges and characteristics of the rail transit domain and, for the first time, unifies UAV-based rail transit inspection within a full-chain“End-Edge-Cloud-Surveillance”framework. This provides a generalizable reference framework for the future deployment of autonomous UAVs in rail infrastructure inspection.