IoT-Based Intelligent Railway Safety System for Crack Detection and Collision Avoidance

DOI

10.2991/978-94-6239-616-6_110How to use a DOI?

Keywords

Internet of Things (IoT); Railway safety systems; Crack detection algorithms; Collision avoidance systems; ResNet50; Deep Residual U-Net; Convolutional Neural Networks; Semantic segmentation

Abstract

The Railway safety systems continue to face significant challenges from undetected track defects, infrastructure obstacles, and potential collision scenarios. Traditional manual inspection protocols and conventional sensor-based monitoring systems frequently demonstrate limitations in terms of processing speed, detection accuracy, and susceptibility to human error. This research presents an IoT-enabled intelligent railway safety framework designed to deliver real-time, automated monitoring solutions. For improved crack and obstacle detection capabilities, the suggested system integrates seismic and ultrasonic sensor technologies. In contrast to conventional vibration sensors that exhibit noise susceptibility, seismic sensors analyze ground vibration patterns to provide more reliable crack detection mechanisms. The collision avoidance component detects trains operating on identical tracks and automatically initiates engine stop protocols to prevent accidents. All critical operational data is transmitted to cloud-based platforms, facilitating real-time monitoring and predictive maintenance strategies. This methodology aims to enhance safety protocols, operational efficiency, and system reliability by minimizing dependence on manual inspection procedures while providing scalable solutions for both urban and rural railway networks. The framework’s performance depends significantly on sensor input quality and consistent data connectivity infrastructure.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - T. Maheshwaran
AU  - D. Seethaaraman
AU  - T. Kaameshwaran
AU  - T. Navin
PY  - 2026
DA  - 2026/03/31
TI  - IoT-Based Intelligent Railway Safety System for Crack Detection and Collision Avoidance
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 1531
EP  - 1539
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_110
DO  - 10.2991/978-94-6239-616-6_110
ID  - Maheshwaran2026
ER  -