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Real-Time Traffic Load Monitoring Framework Based on Deep Learning Model and Statistical Regularities of Vehicle Shape Prior Information

  • 2025
  • OriginalPaper
  • Chapter
Published in:
Authors
Boqiang Xu
Zhijun Shi
Chao Liu
Published in
Proceedings of the 18th East Asia-Pacific Conference on Structural Engineering and Construction, Volume 3
Publisher
Springer Nature Singapore

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Abstract

This chapter introduces a cutting-edge framework for real-time traffic load monitoring, combining deep learning and statistical analysis to revolutionize bridge health monitoring systems. The study focuses on four key areas: vehicle object detection using the YOLO-v5 model, indirect calculation methods for vehicle load application points, statistical analysis of load application points, and tracking and optimal state estimation of vehicle loads. The research presents a method to accurately determine the position of vehicle load application points by simplifying vehicle shapes and using statistical analysis to refine estimates. This approach significantly reduces errors compared to traditional methods. The framework's efficiency, capable of processing at 71FPS, and the integration of Kalman filtering for error reduction are highlighted. The study concludes with successful real-world testing, demonstrating the framework's potential for practical applications in bridge health and traffic management systems.
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Title
Real-Time Traffic Load Monitoring Framework Based on Deep Learning Model and Statistical Regularities of Vehicle Shape Prior Information
Authors
Boqiang Xu
Zhijun Shi
Chao Liu
Copyright Year
2025
Publisher
Springer Nature Singapore
Book
Proceedings of the 18th East Asia-Pacific Conference on Structural Engineering and Construction, Volume 3

Print ISBN: 978-981-9500-89-5

Electronic ISBN: 978-981-9500-90-1

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-981-95-0090-1_66
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