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This is a Experimental Study study.

October 17, 2025

AI-Driven Railway Maintenance for Fault Identification Through Object Detection and Segmentation

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AI-driven models significantly enhance railway maintenance by automating fault detection, improving safety.
The combination of yolo11 and mask r-cnn outperforms baseline models like RTDETR in accuracy and speed.
Deep learning techniques applied to video frames enhance the reliability of identifying track defects.
Integrating object detection and segmentation significantly streamlines the railway inspection process.

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This analysis demonstrates improved fault identification using AI in railway maintenance, highlighting efficiency.

CHINTALAPALLY et al. (2025) studied this question.

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