Autonomous Rail Surface Defect Identification Based on an Improved One-Stage Object Detection Algorithm
Publication: Journal of Performance of Constructed Facilities
Volume 38, Issue 5
Abstract
The rail is an important component of track infrastructure, which withstands repeated wheel loading directly, and its condition is related to the safety of train operation. Thus, accurately identifying the size and location of surface defects in rails helps to optimize maintenance strategies, including adjusting regular monitoring and conducting timely repairs. This approach not only mitigates risks but also enhances work efficiency, which has real economic value and brings safety guarantees. This paper aims to build a data-driven model for rail surface defect identification using photos taken in real lines. Two modules, multidirection rectangular convolution (MRC) and cross-scale (CS) feature extraction, are proposed. The results indicate that the detection and classification of multiple rail surface defects can be automated simultaneously with greater accuracy. Among the defects, spalling sees the most significant boost, and its average detection precision increases from 44.1% to 67%. Moreover, the accuracy for detecting a bright contact band and corrugation exceeds 90%, with 0.995 and 0.915, respectively. Compared with the original You Only Look Once algorithm version 8, the mean of average precision (mAP) of the improved network increases from 85.3% to 88.1% when both models are trained for 300 epochs. Additionally, the precise location and size information of the rail surface defects are obtained through postprocessing, providing support for further intelligent track maintenance.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors acknowledge Projects of Scientific and Technological Research and Development Program of China Railway (L2021G002), National Natural Science Foundation of China (51678445 and 51878661), Key Projects of Shanghai Science and Technology Commission (20dz1203100), fundamental research funds for the central universities (2022-5-ZD-04), and the Department of Transportation Science and Technology Plan Project of Zhejiang Province (Project No. 2023024).
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Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 38 • Issue 5 • October 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Feb 20, 2024
Accepted: May 22, 2024
Published online: Aug 13, 2024
Published in print: Oct 1, 2024
Discussion open until: Jan 13, 2025
ASCE Technical Topics:
- Algorithms
- Business management
- Continuum mechanics
- Defects and imperfections
- Dynamic loads
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Infrastructure
- Materials characterization
- Materials engineering
- Mathematics
- Practice and Profession
- Public administration
- Public health and safety
- Rail transportation
- Railroad tracks
- Railroad trains
- Safety
- Solid mechanics
- Structural dynamics
- Structural engineering
- Transportation engineering
- Vehicle loads
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