Real-Time Railway Track Monitoring Using Yolov8 and Embedded Systems for Automated Defect Detection
Year: 2025
Type: article
Abstract: : This Railway track monitoring is essential for ensuring the safety, reliability, and longevity of rail transportation infrastructure. Traditional inspection methods such as manual surveys and track circuit-based systems are labour intensive, time consuming, and prone to human error, which results in delayed fault detection and increased maintenance risks. Addressing these challenges, this study presents the Railway Track Monitoring Tool (RT-MT), an intelligent real time defect detection system that integrates machine learning with embedded electronics to enhance railway infrastructure monitoring. The RT-MT employs a Raspberry Pi 5 (4GB), a Universal Serial Bus (USB) 1080p camera, a SIM800C GSM module, and a UBLOX NEO 6M Global Positioning System (GPS) module to automate track inspections. The camera captures railway track images which are processed using a You Only Look Once (YOLO) v8 based object detection model deployed on the Raspberry Pi to identify cracks and missing fasteners, which are critical defects affecting track integrity. A dataset consisting of 1,000 crack images and 500 fastener images was used to train the model, ensuring reliable defect classification. When a defect is detected, GPS coordinates are extracted and an alert is sent through a messaging system to maintenance teams for immediate action. A spam prevention mechanism prevents redundant alerts by triggering notifications only when a defect is detected beyond a 50-meter radius from the previous detection. A web-based monitoring system provides real time defect tracking, visualization, and data logging for data driven maintenance decisions. The Railway Track Monitoring Tool, validated on a prototype railway track, offers a cost effective, scalable, and automated solution, addressing the limitations of traditional methods while improving railway safety and operational efficiency.
Author: Dr. Nelwin Raj N. R
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409404229