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 de...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/9193511 |
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| _version_ | 1832551243859361792 |
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| author | Nhat-Duc Hoang Thanh-Canh Huynh Xuan-Linh Tran Van-Duc Tran |
| author_facet | Nhat-Duc Hoang Thanh-Canh Huynh Xuan-Linh Tran Van-Duc Tran |
| author_sort | Nhat-Duc Hoang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-35f534163cd44d438995ff4c81360ae6 |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-35f534163cd44d438995ff4c81360ae62025-02-03T06:01:51ZengWileyAdvances in Civil Engineering1687-80942022-01-01202210.1155/2022/9193511A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine LearningNhat-Duc Hoang0Thanh-Canh Huynh1Xuan-Linh Tran2Van-Duc Tran3Institute of Research and DevelopmentInstitute of Research and DevelopmentInstitute of Research and DevelopmentInternational SchoolDuring 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.http://dx.doi.org/10.1155/2022/9193511 |
| spellingShingle | Nhat-Duc Hoang Thanh-Canh Huynh Xuan-Linh Tran Van-Duc Tran A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning Advances in Civil Engineering |
| title | A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning |
| title_full | A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning |
| title_fullStr | A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning |
| title_full_unstemmed | A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning |
| title_short | A Novel Approach for Detection of Pavement Crack and Sealed Crack Using Image Processing and Salp Swarm Algorithm Optimized Machine Learning |
| title_sort | novel approach for detection of pavement crack and sealed crack using image processing and salp swarm algorithm optimized machine learning |
| url | http://dx.doi.org/10.1155/2022/9193511 |
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