Tong Lei, Limin Jia, Honggui Han, Yong Qin, Zhiyu Li, Haonan Zhang, Zhipeng Wang
Rail fasteners are crucial railroad infrastructure and their health status is directly connected to the safety of traveling trains. UAV-based rail fastener visual inspection has shown strong advantages over traditional inspection techniques. However, existing general-purpose object detection architectures inevitably suffer from the limitation that they can only detect objects that are clearly visible. Consequently, they tend to neglect objects affected by occlusion or shadows and lead to missed detection problem. The industrial application of such models can bring huge safety risks to long-term operations of safety-sensitive railroad systems. Concerning the issues, this paper proposes a visual prior-guided rail fastener integrity detection architecture (RFIDet) to realize coarse-to-fine detection of all rail fasteners, whether normally visible or visually obscured. RFIDet employs a two-stage pipeline: the visual prior guidance (VPG) stage generates standard rail fastener layout representation (SRFLR) for coarse priors, while the precise location search (PLS) stage enables NMS-free refinement using adaptive anchors designed from actual physical distance priors. SRFLR takes full advantage of inherent spatial priors of all rail fasteners to perceive a unified, interconnected, and coarse location distribution. Then all rail fastener candidates activated by those coarse locations are further trained to search and regress refined offsets to the final bounding boxes. Structural loss functions for both stages are customized to facilitate the detection of individual fasteners while constraining the overall spatial distribution of all fasteners. Experiments have verified the effectiveness and better robustness of the proposed RFIDet with the mAP50value increased by at least 5.9% compared to a series of general-purpose SOTA YOLO detectors. RFIDet outperforms the comparing algorithms especially when coming across unexpected occlusions or shadows.