A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning
During the phase of periodic survey, sealed crack and crack in asphalt pavement surface should be detected accurately. Moreover, the capability of identifying these two defects can help reduce the false-positive rate for pavement crack detection. Because crack and sealed crack are both line-based defects and may resemble each other in shape, this study puts forward an innovative method based on computer vision for detecting sealed crack and crack. This method is an integration of feature extraction based on image processing and metaheuristic optimized machine learning. Image processing is used to compute features that characterize visual appearance and texture of the pavement image. Subsequently, Salp Swarm Algorithm integrated with multiclass support vector machine is employed for pattern recognition. Based on experimental results, the newly developed method has achieved the most desired predictive performance with an accuracy rate = 91.33% for crack detection and 92.83% for sealed crack detection.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/16878086
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Supplemental Notes:
- © 2022 Nhat-Duc Hoang et al.
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Authors:
- Hoang, Nhat-Duc
- Huynh, Thanh-Canh
- Tran, Xuan-Linh
- Tran, Van-Duc
- Publication Date: 2022-3
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: Article ID 9193511
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Serial:
- Advances in Civil Engineering
- Volume: 2022
- Publisher: Wiley
- ISSN: 1687-8086
- EISSN: 1687-8094
- Serial URL: https://onlinelibrary.wiley.com/journal/7074
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Algorithms; Detection and identification; Image processing; Machine learning; Pavement cracking; Pavement maintenance
- Subject Areas: Highways; Pavements;
Filing Info
- Accession Number: 01843374
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 25 2022 10:06AM