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

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.