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Ravikant Mordia

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2 papers indexed here

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Open access Sep 2026

Comparative Evaluation of Deep Learning and Hybrid CNN–Random Forest Models for Multi-Class Defect Identification in Railway Tracks

Railway infrastructure is fundamental to national logistics and public transportation systems. Defects in railway tracks, such as squats, shelling, spalling, flaking, burned rails, and joint issues, can significantly compromise operational safety. Traditional inspection methods, which rely heavily on manual labour, are...

Ravikant Mordia, A. K. Verma · 0 citations
Open access Aug 2026

High-precision railway track defect identification using YOLOv11: a comparative evaluation with Roboflow 3.0

YOLOv11 demonstrates substantially superior fault-detection performance attributable to its refined multi-scale feature extraction, efficient detection head, and modern backbone optimization, compared with the Roboflow Train 3.0 pipeline on a binary railway defect detection task under fully controlled conditions.

Ravikant Mordia, A. K. Verma · 0 citations

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