Appl. Sci. 2026, 16(17), 8714; https://doi.org/10.3390/app16178714 (registering DOI) - 2 Sep 2026
Abstract
Rail internal defects pose significant threats to railway operational safety, and ultrasonic B-scan imaging provides an effective nondestructive testing approach for visualizing subsurface rail conditions. However, manual interpretation of B-scan images is time-consuming and depends heavily on operator experience. To overcome this problem,
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Rail internal defects pose significant threats to railway operational safety, and ultrasonic B-scan imaging provides an effective nondestructive testing approach for visualizing subsurface rail conditions. However, manual interpretation of B-scan images is time-consuming and depends heavily on operator experience. To overcome this problem, a Multi-scale Dense Attention Network (MSDA-Net) is proposed for automatic classification of rail ultrasonic B-scan images. The proposed network integrates dilated convolution, multi-scale dense feature extraction, and attention-based feature recalibration to capture defect features at different spatial scales while enhancing discriminative defect-related regions. A dataset containing 536 ultrasonic B-scan images is constructed for model training and evaluation, and the B-scan images are collected from multiple railway sections. Experimental results show that MSDA-Net achieved an accuracy of 0.909, with a macro Precision of 0.867, macro Recall of 0.845, and macro F1-score of 0.848. Comparative and ablation experiments further demonstrate the effectiveness of the proposed network structure for rail defect classification.
Full article
(This article belongs to the Special Issue Defect Evaluation and Nondestructive Testing)
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