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

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.

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Railways defect; corrugation; squat; neural network; VGG16; YOLOv5.
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Computer Science and Information Technology > FSKTM
Depositing User:	Mr. Mohamad Zulkhibri Rahmad
Date Deposited:	27 Jan 2025 02:43
Last Modified:	27 Jan 2025 02:44
URI:	http://eprints.uthm.edu.my/id/eprint/12452

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