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All Journal Bulletin of Electrical Engineering and Informatics
Mohd Yusof, Najiha ‘Izzaty
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Electrical & Electronics Engineering

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Assessing the performance of YOLOv5, YOLOv6, and YOLOv7 in road defect detection and classification: a comparative study Mohd Yusof, Najiha ‘Izzaty; Sophian, Ali; Mohd Zaki, Hasan Firdaus; Bawono, Ali Aryo; Embong, Abd Halim; Ashraf, Arselan
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6317

Abstract

Road defect inspection is a crucial task in maintaining a good transportation infrastructure as road surface distress can impact user’s comfortability, reduce the lifetime of vehicles’ parts, and cause road casualties. In recent years, machine learning has been adapted widely in various fields, including object detection, thanks to its superior performance and the availability of high computing power which is generally needed for its model training. Many works have reported using machine-learning-based object detection algorithms to detect defects, such as cracks in buildings and roads. In this work, YOLOv5, YOLOv6 and YOLOv7 models have been implemented and trained using a custom dataset of road cracks and potholes and their performances have been evaluated and compared. Experiments on the dataset show that YOLOv7 has the highest performance with mAP@0.5 score of 79.0% and an inference speed of 0.47 m for 255 test images.
Co-Authors Ashraf, Arselan Bawono, Ali Aryo Embong, Abd Halim Mohd Zaki, Hasan Firdaus Sophian, Ali