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To evaluate rail damage through image analysis using artificial neural networks for enhanced accuracy.

March 15, 2026Applied SciencesOpen Access

Evaluation of Rail Damage Using Image Analysis Based on an Artificial Neural Network

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To evaluate rail damage through image analysis using artificial neural networks for enhanced accuracy.
Developed a rail damage assessment technique using deep learning and image analysis framework
Collected rail specimens and performed scanning electron microscopy to analyze surface damage characteristics
Applied a convolutional neural network to assess internal crack conditions from visual data
Integrated the model into a rail damage diagnosis app for practical use at rail sites
Achieved an average accuracy of 94.9% surpassing other algorithms
Standard deviation of diagnosis results ranged from 0.2–1.5%, indicating minimal user variability
Demonstrated that the proposed technique effectively reduces human error in inspections

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Experimental evaluation demonstrates deep learning improves accuracy in diagnosing rolling contact fatigue in rails, reducing human error.

Choi et al. (2026) studied this question.

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Experimental evaluation demonstrates deep learning improves accuracy in diagnosing rolling contact fatigue in rails, reducing human error.