Railway Track Crack Detection

Keeping railway tracks safe really depends on spotting cracks, corrugation, and weld failures as soon as possible. The old-school ways—manual inspections, ultrasonic testing vehicles, and regular maintenance—just take too long, cost too much, and can’t watch the tracks nonstop. So, in this work, the goal is to build a smarter Railway Track Crack Detection System that listens for trouble. The idea is to grab the sounds that happen when train wheels meet the rails, then dig into those sounds using signal processing techniques like Mel-Frequency Cepstral Coefficients, Spectral Feature Extraction, and Fractal Dimension Analysis. These methods help pull out details from the noise that can reveal if something’s wrong. But there’s a catch: there aren’t enough examples of damaged tracks to train a good model. To get around that, we use Generative AI to create new, realistic defect samples and balance the dataset. Once we’ve got solid data, we turn to machine learning—Random Forest, Support Vector Machine, XGBoost, and some ensemble methods—to teach the system how to tell healthy tracks from damaged ones. The end goal? Faster, smarter, and more reliable railway safety.