Automated Pavement Cracks Detection and Classification Using Deep Learning
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913387397513216 |
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| author | Nafaa, Selvia Essam, Hafsa Ashour, Karim Emad, Doaa Mohamed, Rana Elhenawy, Mohammed Ashqar, Huthaifa I. Hassan, Abdallah A. Alhadidi, Taqwa I. |
| author_facet | Nafaa, Selvia Essam, Hafsa Ashour, Karim Emad, Doaa Mohamed, Rana Elhenawy, Mohammed Ashqar, Huthaifa I. Hassan, Abdallah A. Alhadidi, Taqwa I. |
| contents | Monitoring asset conditions is a crucial factor in building efficient transportation asset management. Because of substantial advances in image processing, traditional manual classification has been largely replaced by semi-automatic/automatic techniques. As a result, automated asset detection and classification techniques are required. This paper proposes a methodology to detect and classify roadway pavement cracks using the well-known You Only Look Once (YOLO) version five (YOLOv5) and version 8 (YOLOv8) algorithms. Experimental results indicated that the precision of pavement crack detection reaches up to 67.3% under different illumination conditions and image sizes. The findings of this study can assist highway agencies in accurately detecting and classifying asset conditions under different illumination conditions. This will reduce the cost and time that are associated with manual inspection, which can greatly reduce the cost of highway asset maintenance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07674 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Pavement Cracks Detection and Classification Using Deep Learning Nafaa, Selvia Essam, Hafsa Ashour, Karim Emad, Doaa Mohamed, Rana Elhenawy, Mohammed Ashqar, Huthaifa I. Hassan, Abdallah A. Alhadidi, Taqwa I. Computer Vision and Pattern Recognition Computers and Society Monitoring asset conditions is a crucial factor in building efficient transportation asset management. Because of substantial advances in image processing, traditional manual classification has been largely replaced by semi-automatic/automatic techniques. As a result, automated asset detection and classification techniques are required. This paper proposes a methodology to detect and classify roadway pavement cracks using the well-known You Only Look Once (YOLO) version five (YOLOv5) and version 8 (YOLOv8) algorithms. Experimental results indicated that the precision of pavement crack detection reaches up to 67.3% under different illumination conditions and image sizes. The findings of this study can assist highway agencies in accurately detecting and classifying asset conditions under different illumination conditions. This will reduce the cost and time that are associated with manual inspection, which can greatly reduce the cost of highway asset maintenance. |
| title | Automated Pavement Cracks Detection and Classification Using Deep Learning |
| topic | Computer Vision and Pattern Recognition Computers and Society |
| url | https://arxiv.org/abs/2406.07674 |