Abstract
The demand for efficient track inspection systems in the rapidly evolving rail transportation field is more pronounced than ever. Hence, this study combines deep learning and edge computing for railroad track component inspection, focusing on the YOLO-NAS architecture. Our objective was twofold: to harness the advantages of YOLO-NAS for accurate and high-speed detection while addressing the computational constraints of edge devices. Consequently, YOLO-NAS-S-PTQ model achieved a remarkable balance, with 74.77% mAP and 92.20 FPS, on the NVIDIA Jetson Orin platform. By deploying this model on an edge device and utilizing a multiprocessor pipeline, we observed an inference speed of 60.468 FPS, which was nearly double the rate of its single-threaded counterpart. Field tests further confirmed the efficiency of the model, demonstrating a recall rate of 80.77% and an accuracy of 96.64%. These findings underscore the potential of YOLO-NAS in transforming traditional rail component inspection methods, significantly reducing human interventions and potential errors.
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