Railway Track Crack Detection System

Abstract

Railway safety depends heavily on the condition of tracks, yet traditional inspection methods rely on manual checks that are slow, costly, and prone to human error. This project introduces an railway crack detection system designed for continuous and automated monitoring. The system uses low-cost infrared sensors integrated with an ESP32 microcontroller and ESP32-CAM module, mounted on a mobile platform that scans railway tracks for surface irregularities. When a crack is detected, the system captures high-quality images and records GPS location and time details. This information is transmitted wirelessly to a cloud-based dashboard, allowing maintenance teams to receive instant alerts and assess issues remotely. By enabling early fault detection, the system supports preventive maintenance, reduces downtime, and improves overall railway safety. Its modular and scalable design allows future upgrades, such as more advanced machine learning techniques and integration with existing railway management systems, making it a practical and cost-effective solution for modern railway infrastructure monitoring.

Introduction

Railway tracks are prone to wear and cracks, which can lead to severe accidents if undetected. Traditional manual inspections are time-consuming, labor-intensive, and error-prone, lacking continuous monitoring. To address this, the study proposes an IoT-based Railway Crack Detection System using IR sensors and an ESP32-CAM module for real-time monitoring. The IR sensors detect surface irregularities, while the camera provides visual confirmation, reducing false alarms. Detected cracks are transmitted via Wi-Fi to a cloud-based dashboard for remote monitoring, with GPS coordinates for precise location tracking.

The system is mounted on a mobile inspection unit that autonomously scans tracks. IR sensors detect abnormalities, the ESP32 processes signals against thresholds, and the ESP32-CAM captures images. Alerts are sent to the monitoring platform, and data is stored for maintenance planning.

Results showed high accuracy in crack detection, reliable wireless data transmission, and effective real-time monitoring. GPS mapping enables precise location identification, and the robotic platform operates smoothly with collision avoidance. The system proved efficient, scalable, and practical for real-time railway safety monitoring, with slight limitations under low-light conditions.

References

[1] P. Shalini and D. Gopalakrishnan, “Intelligent Railway Crack Detection System Using IoT and Deep Learning,” International Journal of Computer Applications (IJCA), vol. 176, no. 23, pp. 12–18, 2021. [Online]. Available: https://www.ijcaonline.org [2] V. Muthukumaran and R. Senthil Kumar, “Design and Implementation of Railway Track Crack Detection System Using IR Sensor and GSM,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 6, no. 4, pp. 2894–2899, Apr. 2017. [Online]. Available: https://www.ijareeie.com [3] C. Yadav, S. Kumar, and S. K. Singh, “Wireless Sensor Network for Railway Track Fault Detection,” International Journal of Scientific & Engineering Research (IJSER), vol. 9, no. 6, pp. 102–107, Jun. 2018. [Online]. Available: https://www.ijser.org [4] A. Raza and F. Qureshi, “Low-Cost Automated Detection and Reporting System for Railway Track Faults,” IEEE Conference on Emerging Technologies, pp. 1–5, 2020. [Online]. Available: https://ieeexplore.ieee.org [5] Espressif Systems, “ESP32 Technical Reference Manual,” Espressif Systems Inc., 2022. [Online]. Available: https://www.espressif.com/documentation [6] AI-Thinker, “ESP32-CAM Development Board Datasheet and Camera Interface,” AI-Thinker Technologies, 2021. [Online]. Available: https://randomnerdtutorials.com

Copyright

Copyright © 2025 Pramod ., Raghvendra , Santosh , Prof. Rajkumar B.. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.