Enhancing Railway Safety: A Machine Learning Approach for Automated Detection of Missing Track Bolts

Railways are a critical component of transportation infrastructure, enduring significant physical stress daily due to the massive weight of trains and their cargo. This constant use can lead to the wear and tear of railway components, posing safety risks. One essential part of the railway infrastructure is the track bolts, which secure the rails in place, ensuring safe train traversal and preventing derailments that could cause severe injuries to passengers. This study investigates a machine learning algorithm approach to detect missing track bolts from image and video data automatically. The aim is to develop an algorithm that accurately identifies missing bolts on railways, thereby mitigating safety risks associated with poorly maintained tracks and streamlining maintenance processes. Therefore, various machine learning algorithms are evaluated using a specially curated dataset to detect missing track bolts to provide more efficient tools for detecting missing track bolts, contributing to railway operations’ reliability and safety.