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All Journal Bulletin of Electrical Engineering and Informatics Applied Research and Smart Technology (ARSTech)
Ashraf, Arselan
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Automotive Engineering Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Physics

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Journal : Bulletin of Electrical Engineering and Informatics

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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.
Machine learning-based pavement crack detection, classification, and characterization: a review Ashraf, Arselan; Sophian, Ali; Shafie, Amir Akramin; Gunawan, Teddy Surya; Ismail, Norfarah Nadia
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5345

Abstract

The detection, classification, and characterization of pavement cracks are critical for maintaining safe road conditions. However, traditional manual inspection methods are slow, costly, and pose risks to inspectors. To address these issues, this article provides a comprehensive overview of state-of-the-art machine vision and machine learning-based techniques for pavement crack detection, classification, and characterization. The paper explores the process flow of these systems, including both machine learning and traditional methodologies. The paper focuses on popular artificial intelligence (AI) techniques like support vector machines (SVM) and neural networks. It underscores the significance of utilizing image processing methods for feature extraction in order to detect cracks. The paper also discusses significant advancements made through deep learning strategies. The main objectives of this research are to improve efficiency and effectiveness in pavement crack detection, reduce inspection costs, and enhance safety. Additionally, the article presents data gathering approaches, various datasets for developing road crack detection models, and compares different models to demonstrate their advantages and limitations. Finally, the paper identifies open challenges in the field and provides valuable insights for future research and development efforts. Overall, this paper highlights the potential of AI-based techniques to revolutionize pavement maintenance practices and significantly improve road safety.
Co-Authors Bawono, Ali Aryo Embong, Abd Halim Ismail, Norfarah Nadia Mohd Yusof, Najiha ‘Izzaty Mohd Zaki, Hasan Firdaus Shafie, Amir Akramin Sophian, Ali Teddy Surya Gunawan Wani, Sidrah Fayaz