Aug 31, 2026 · cs.CVJ/K move · Enter open · S save
Quan Hao, Chenxi Zhang, Ziyang Tao, Yuyuan Zhou+5
1The School of Information Science and Technology, Beijing University of Technology · 2Pratt School of Engineering, Duke University · 3Academy of Mathematics and Systems Science, Chinese Academy of Sciences · School of Mathematical Sciences, University of Chinese Academy of Sciences · Institute of Automation, Chinese Academy of Sciences
Railway foreign object detection (RFOD) is critical to safe railway operation, yet scarce real positive samples incompletely represent task-relevant variations in object scale, intrusion relation, railway scene, illumination, and adverse weather. Existing synthetic augmentation can improve RFOD detection, but its gains lack an explicit account of the task-relevant deficiencies complemented by the generated data. We therefore introduce RailSyn, a diagnosis-guided framework comprising a real-referenced Inspector and a requirement-aligned Generator. The Inspector constructs a variable-radius empirical cover from finite real observations to localize candidate completion regions and profile synthetic pools. The resulting audit identifies railway-context, intrusion-semantic, and visual-consistency requirements; the Generator addresses them through domain adaptation, agent-planned placement and physical contact relations, and plan-consistent conditional refinement. Using the Inspector, we further trace representation-space changes across generation variants; the complete system attains a local-shell occupation of
Cgap to 13.64%, which measures generated coverage of real-derived completion regions. Extensive experiments show AP50--95 gains of up to 4.9 points and consistent improvements across nine mainstream detectors, demonstrating broad cross-architecture utility.