A Novel Machine Learning Framework for Real-Time Railway Crack and Squat Detection

Abstract: Preventing accidents and maximizing maintenance efforts require secure railway infrastructure. This study introduces a machine learning-based method for detecting cracks in railway tracks that makes use of computational intelligence and image processing techniques. The system analyses and tracks photos, extracts fault features, and accurately classifies abnormalities using MATLAB. The suggested system efficiently detects fractures, squats, and structural flaws by combining deep learning algorithms, edge detection methods, and pattern recognition models. Furthermore, a hardware module based on Arduino analyses identified defects and sends real-time notifications to a central monitoring system, allowing for prompt remedial action. By enabling proactive resource allocation and ongoing track monitoring, this connection improves predictive maintenance, which in turn raises railway safety and operating effectiveness