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All Journal JOIV : International Journal on Informatics Visualization
Md Ali, Zulhelmi
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Computer Science & IT

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Journal : JOIV : International Journal on Informatics Visualization

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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
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2756

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.
Co-Authors Abdul Hamid, Nor Aziati Ahmad Shaubari, Ezak Fadzrin Amzah, Azmale Mohd Amin Mohd Yunus, Mohd Amin Mohd Yazed, Muhammad Syukri