Staff View: Corrugation and Squat Classification and Detection with VGG16 and YOLOv5 Neural Network Models

Corrugation and Squat Classification and Detection with VGG16 and YOLOv5 Neural Network Models

Railway track defects in Malaysia pose significant risks of train derailments and accidents, underscoring the urgency for early and accurate defect detection and classification. This study presents a novel approach utilizing deep learning models, VGG16 and YOLOv5, for detecting and classifying rail...

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Main Authors:	Mohd Yazed, Muhammad Syukri, Mohd Yunus, Mohd Amin, Ahmad Shaubari, Ezak Fadzrin, Abdul Hamid, Nor Aziati, Amzah, Azmale, Md Ali, Zulhelmi
Format:	Article
Language:	English
Published:	Joiv 2024
Subjects:	QA Mathematics
Online Access:	http://eprints.uthm.edu.my/12452/1/J17942_8b5dd786ec258acd58dea5dc6212157e.pdf http://eprints.uthm.edu.my/12452/
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id	my.uthm.eprints.12452
record_format	eprints
spelling	my.uthm.eprints.124522025-01-27T02:44:49Z http://eprints.uthm.edu.my/12452/ Corrugation and Squat Classification and Detection with VGG16 and YOLOv5 Neural Network Models Mohd Yazed, Muhammad Syukri Mohd Yunus, Mohd Amin Ahmad Shaubari, Ezak Fadzrin Abdul Hamid, Nor Aziati Amzah, Azmale Md Ali, Zulhelmi QA Mathematics Railway track defects in Malaysia pose significant risks of train derailments and accidents, underscoring the urgency for early and accurate defect detection and classification. This study presents a novel approach utilizing deep learning models, VGG16 and YOLOv5, for detecting and classifying railway track defects, explicitly focusing on corrugation and squat defects. The research's uniqueness lies in its application of these specific models and the composition of a dataset collected from extensive field measurements and inspections across various railway tracks within the Track Network Maintenance Ampang Line in Malaysia. The results demonstrate that these models achieve high precision in defect classification and detection of defects by more than 80%. The proposed methodology provides the railway industry with a powerful tool to streamline maintenance planning and prioritize defect remediation efficiently. Early defect detection can prevent potential accidents and improve safety and operational efficiency. Future studies can expand on these findings by exploring the extension of the proposed techniques to address other types of rail defects. Incorporating a diverse range of scenarios and operating conditions in the dataset could further enhance the models' performance and generalization. Real-time deployment and integration with existing maintenance systems are crucial for practical adoption. This research has strengths but acknowledges limitations. Additional evaluation metrics and a diverse dataset are essential for model performance. Leveraging deep learning models offers a reliable solution for railway maintenance, enhancing safety and efficiency. Addressing these limitations will drive proactive defect management, ensuring safe and reliable railway networks. Joiv 2024 Article PeerReviewed text en http://eprints.uthm.edu.my/12452/1/J17942_8b5dd786ec258acd58dea5dc6212157e.pdf Mohd Yazed, Muhammad Syukri and Mohd Yunus, Mohd Amin and Ahmad Shaubari, Ezak Fadzrin and Abdul Hamid, Nor Aziati and Amzah, Azmale and Md Ali, Zulhelmi (2024) Corrugation and Squat Classification and Detection with VGG16 and YOLOv5 Neural Network Models. International Journal On Informatics Visualization, 8 (2). pp. 916-922.
institution	Universiti Tun Hussein Onn Malaysia
building	UTHM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tun Hussein Onn Malaysia
content_source	UTHM Institutional Repository
url_provider	http://eprints.uthm.edu.my/
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Mohd Yazed, Muhammad Syukri Mohd Yunus, Mohd Amin Ahmad Shaubari, Ezak Fadzrin Abdul Hamid, Nor Aziati Amzah, Azmale Md Ali, Zulhelmi Corrugation and Squat Classification and Detection with VGG16 and YOLOv5 Neural Network Models
description	Railway track defects in Malaysia pose significant risks of train derailments and accidents, underscoring the urgency for early and accurate defect detection and classification. This study presents a novel approach utilizing deep learning models, VGG16 and YOLOv5, for detecting and classifying railway track defects, explicitly focusing on corrugation and squat defects. The research's uniqueness lies in its application of these specific models and the composition of a dataset collected from extensive field measurements and inspections across various railway tracks within the Track Network Maintenance Ampang Line in Malaysia. The results demonstrate that these models achieve high precision in defect classification and detection of defects by more than 80%. The proposed methodology provides the railway industry with a powerful tool to streamline maintenance planning and prioritize defect remediation efficiently. Early defect detection can prevent potential accidents and improve safety and operational efficiency. Future studies can expand on these findings by exploring the extension of the proposed techniques to address other types of rail defects. Incorporating a diverse range of scenarios and operating conditions in the dataset could further enhance the models' performance and generalization. Real-time deployment and integration with existing maintenance systems are crucial for practical adoption. This research has strengths but acknowledges limitations. Additional evaluation metrics and a diverse dataset are essential for model performance. Leveraging deep learning models offers a reliable solution for railway maintenance, enhancing safety and efficiency. Addressing these limitations will drive proactive defect management, ensuring safe and reliable railway networks.
format	Article
author	Mohd Yazed, Muhammad Syukri Mohd Yunus, Mohd Amin Ahmad Shaubari, Ezak Fadzrin Abdul Hamid, Nor Aziati Amzah, Azmale Md Ali, Zulhelmi
author_facet	Mohd Yazed, Muhammad Syukri Mohd Yunus, Mohd Amin Ahmad Shaubari, Ezak Fadzrin Abdul Hamid, Nor Aziati Amzah, Azmale Md Ali, Zulhelmi
author_sort	Mohd Yazed, Muhammad Syukri
title	Corrugation and Squat Classification and Detection with VGG16 and YOLOv5 Neural Network Models
title_short	Corrugation and Squat Classification and Detection with VGG16 and YOLOv5 Neural Network Models
title_full	Corrugation and Squat Classification and Detection with VGG16 and YOLOv5 Neural Network Models
title_fullStr	Corrugation and Squat Classification and Detection with VGG16 and YOLOv5 Neural Network Models
title_full_unstemmed	Corrugation and Squat Classification and Detection with VGG16 and YOLOv5 Neural Network Models
title_sort	corrugation and squat classification and detection with vgg16 and yolov5 neural network models
publisher	Joiv
publishDate	2024
url	http://eprints.uthm.edu.my/12452/1/J17942_8b5dd786ec258acd58dea5dc6212157e.pdf http://eprints.uthm.edu.my/12452/
_version_	1823094309755289600
score	13.244413

Corrugation and Squat Classification and Detection with VGG16 and YOLOv5 Neural Network Models

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