Rail Flaw B-Scan Image Analysis Using a Hierarchical Classification Model

Abstract

As railway traffic volumes and train speeds increase, rail maintenance is becoming more crucial to prevent catastrophic failures. This study aimed to develop an artificial intelligence (AI)-based solution for automatic rail flaw detection using ultrasound sensors to overcome the limitations of traditional inspection methods. Ultrasound sensors are well-suited for identifying structural abnormalities in rails. However, conventional inspection techniques like rail-walking are time-consuming and rely on human expertise, risking detection errors. To address this, a hierarchical classification model was proposed integrating ultrasound B-scan images and machine learning. It involved a two-stage approach—model A for fuzzy classification followed by Model EfficientNet-B7 was identified as the most effective architecture for both models through network comparisons. Experimental results demonstrated the model's ability to accurately detect rail flaws, achieving 88.56% accuracy. It could analyze a single ultrasound image sheet within 0.45 s. An AI-based solution using ultrasound sensors and hierarchical classification shows promise for automated, rapid, and reliable rail flaw detection to support safer railway infrastructure inspection and maintenance activities.