Research on Real-Time Detection System of Rail Surface Defects Based on Deep Learning

The heavy workload of rail track inspection makes it time consuming, and thus calls out a real-time inspection algorithm to achieve precise and efficient detection. In this study, we developed a real-time detection system for rail surface. Our system utilizes machine vision and real-time algorithms...

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Bibliographic Details
Published in:IEEE sensors journal 2024-07, Vol.24 (13), p.21157-21167
Main Authors: Yaodong, Wang, Hang, Yu, Baoqing, Guo, Hongmei, Shi, Zujun, Yu
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification algorithms
Computational modeling
Deep learning
Defect detection
Edge computing
Inspection
Machine vision
rail surface
Rails
Railway tracks
Real time
real-time detection system
Real-time systems
Surface defects
YOLO
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Summary:The heavy workload of rail track inspection makes it time consuming, and thus calls out a real-time inspection algorithm to achieve precise and efficient detection. In this study, we developed a real-time detection system for rail surface. Our system utilizes machine vision and real-time algorithms to ensure efficient and fast inspections. Edge computing device is used for real-time detection of track defect. To increase detection accuracy and speed, we optimized the YOLOv5 structure by introducing depth-separable convolution and reparameterization methods. Through training and evaluating the model on a dataset of rail surface defects, we achieved a mean average precision (mAP) of 83.2% and a detection speed of 51 FPS on edge computing devices. The performance of model outstrips that of other one-stage algorithms and backbone network detection results, as it exhibits high accuracy and speed. This achievement lays the groundwork for realizing real-time detection of rail defects and augmenting railroad safety.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3402730