FAULT DETECTION IN RAILWAY TRACKS USING IMAGE CLASSIFICATION

FAULT DETECTION IN RAILWAY TRACKS USING IMAGE CLASSIFICATION

Registration ID: IJNRD_209560

Published ID: IJNRD2311265

DOI: Click Here to Get

Srinivaas Reddy , Nikita Mali , Amaan Shaikh , Dr. Rahul Sharma

Computer Vision, Transfer Learning, Machine Learning, Image Processing, Image Classification.

Railway track crack detection is a critical aspect of railway infrastructure maintenance, aimed at ensuring passenger and freight safety and preventing potential accidents. The railway department is implementing several creative approaches to improve the efficiency of the inspection procedure. Various technologies, such as the Computer Vision-Based method, have been investigated in the past to detect defects on rail surfaces, but complete automation is still a long way off. Few countries utilize Deep Learning algorithms to monitor and manage the condition of train rails. This project presents a Fault detection system based on machine learning, computer vision and Image Classification based techniques to automate the process of detecting cracks in railway tracks. The methodology involves Transfer Learning using VGG19 and the collection of a diverse dataset of annotated railway track images, including both defective (cracked) and non-defective (crack-free) samples. The Images are pre-processed using various pre-processing techniques and then Machine learning algorithms are applied to train a model on this pre-processed dataset, enabling it to distinguish between defective and non-defective tracks. The proposed system offers several advantages, including early detection of cracks, reduced maintenance costs, and improved safety measures. By automating the crack detection process, the system minimizes the need for manual inspections and enables timely maintenance actions. The results demonstrate the effectiveness of the fault detection system in accurately identifying cracks on railway tracks, thereby contributing to enhanced railway safety and efficient maintenance practices. The results later are compared with RESNET50 and GoogleNet models, to understand which model gives more accurate results. The successful implementation of this system can lead to significant improvements in railway infrastructure management and overall transportation safety. IndexTerms - Computer Vision, Transfer Learning, Machine Learning, Image Processing, Image Classification.

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"FAULT DETECTION IN RAILWAY TRACKS USING IMAGE CLASSIFICATION ", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 11, page no.c518-c539, November-2023, Available :https://ijnrd.org/papers/IJNRD2311265.pdf

Volume 8 Issue 11, November-2023

Pages : c518-c539

Paper Reg. ID: IJNRD_209560

Published Paper Id: IJNRD2311265

Downloads: 000121123

Research Area: Computer Science & Technology 

Country: Pune, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2311265.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2311265

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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