Abstract :

Ensuring The Safety And Reliability Of Railway Networks Requires Efficient And Accurate Track Inspection. This Project Introduces An Automated Railway Track Fault Detection System Using Deep Neural Networks To Enhance The Accuracy And Speed Of Inspections. TrackNet Employs A Multiphase Deep Learning Approach. First, A U-Net Model Performs Segmentation To Extract Railway Tracks And Identify The Region Of Interest (ROI). Next, A ResNet Or DenseNet Classifier Analyzes The Segmented Region To Detect True Defects While Filtering Out False Alarms Caused By Environmental Noise, Such As Debris Or Markings. This Approach Improves Detection Accuracy And Minimizes Manual Review Efforts. The System Is Implemented Using Python And Django, With A Flask-based Web Interface For Real-time Track Condition Monitoring. The Model Is Scalable And Efficient, Making It Suitable For Industrial Deployment. By Leveraging Deep Neural Networks And Image Processing, This Project Aims To Provide A Fast, Reliable, And Costeffective Railway Track Inspection Solution, Improving Safety And Operational Efficiency

Published:

25-8-2025

Issue:

Vol. 25 No. 8 (2025)

Page Nos:

432-439

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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