An Advanced Otsu Method Integrated with Edge Detection and Decision Tree for Crack Detection in Highway Transportation Infrastructure
The detection of various cracks on pavement surfaces has drawn more and more attention from pavement maintenance engineers. In the traditional pavement image segmentation, due to the small area of the pavement cracks, the gray level of crack pixels only accounts for a very small portion in the grays...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/9205509 |
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| _version_ | 1832560537957826560 |
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| author | Haihang Han Hanyu Deng Qiao Dong Xingyu Gu Tianjie Zhang Yangyang Wang |
| author_facet | Haihang Han Hanyu Deng Qiao Dong Xingyu Gu Tianjie Zhang Yangyang Wang |
| author_sort | Haihang Han |
| collection | DOAJ |
| description | The detection of various cracks on pavement surfaces has drawn more and more attention from pavement maintenance engineers. In the traditional pavement image segmentation, due to the small area of the pavement cracks, the gray level of crack pixels only accounts for a very small portion in the grayscale histogram, making it difficult to segment. This paper developed an improved Otsu method integrated with edge detection and a decision tree classifier for cracking identification in asphalt pavements. An image preprocessing approach including Gaussian function-based spatial filtering and top-hat transform is firstly proposed to reduce the influence of poor shading and lighting effects significantly. Four edge detection operators including Prewitt, Sobel, Gauss–Laplace (LoG), and Canny are evaluated. The Canny edge detection has demonstrated outstanding performance in crack detection; this algorithm helps to obtain more details of both cracks and noises. The Sobel and LoG operators show similar image segmentation and retain fewer noises. The decision tree classifier based on the ID3 algorithm can effectively classify different types of cracks including transverse, longitudinal, and block ones. |
| format | Article |
| id | doaj-art-9885d89c26f849739d774128bd610b5c |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-9885d89c26f849739d774128bd610b5c2025-02-03T01:27:23ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422021-01-01202110.1155/2021/92055099205509An Advanced Otsu Method Integrated with Edge Detection and Decision Tree for Crack Detection in Highway Transportation InfrastructureHaihang Han0Hanyu Deng1Qiao Dong2Xingyu Gu3Tianjie Zhang4Yangyang Wang5Zhejiang Scientific Research Institute of Transport, Zhejiang Provincial Key Lab for Detection and Maintenance Technology of Road and Bridge, Hangzhou, Zhejiang 311305, ChinaSchool of Transportation, National Demonstration Center for Experimental Road and Traffic Engineering Education, Southeast University, Nanjing, Jiangsu 211189, ChinaSchool of Transportation, National Demonstration Center for Experimental Road and Traffic Engineering Education, Southeast University, Nanjing, Jiangsu 211189, ChinaSchool of Transportation, National Demonstration Center for Experimental Road and Traffic Engineering Education, Southeast University, Nanjing, Jiangsu 211189, ChinaZhejiang Scientific Research Institute of Transport, Zhejiang Provincial Key Lab for Detection and Maintenance Technology of Road and Bridge, Hangzhou, Zhejiang 311305, ChinaZhejiang Scientific Research Institute of Transport, Zhejiang Provincial Key Lab for Detection and Maintenance Technology of Road and Bridge, Hangzhou, Zhejiang 311305, ChinaThe detection of various cracks on pavement surfaces has drawn more and more attention from pavement maintenance engineers. In the traditional pavement image segmentation, due to the small area of the pavement cracks, the gray level of crack pixels only accounts for a very small portion in the grayscale histogram, making it difficult to segment. This paper developed an improved Otsu method integrated with edge detection and a decision tree classifier for cracking identification in asphalt pavements. An image preprocessing approach including Gaussian function-based spatial filtering and top-hat transform is firstly proposed to reduce the influence of poor shading and lighting effects significantly. Four edge detection operators including Prewitt, Sobel, Gauss–Laplace (LoG), and Canny are evaluated. The Canny edge detection has demonstrated outstanding performance in crack detection; this algorithm helps to obtain more details of both cracks and noises. The Sobel and LoG operators show similar image segmentation and retain fewer noises. The decision tree classifier based on the ID3 algorithm can effectively classify different types of cracks including transverse, longitudinal, and block ones.http://dx.doi.org/10.1155/2021/9205509 |
| spellingShingle | Haihang Han Hanyu Deng Qiao Dong Xingyu Gu Tianjie Zhang Yangyang Wang An Advanced Otsu Method Integrated with Edge Detection and Decision Tree for Crack Detection in Highway Transportation Infrastructure Advances in Materials Science and Engineering |
| title | An Advanced Otsu Method Integrated with Edge Detection and Decision Tree for Crack Detection in Highway Transportation Infrastructure |
| title_full | An Advanced Otsu Method Integrated with Edge Detection and Decision Tree for Crack Detection in Highway Transportation Infrastructure |
| title_fullStr | An Advanced Otsu Method Integrated with Edge Detection and Decision Tree for Crack Detection in Highway Transportation Infrastructure |
| title_full_unstemmed | An Advanced Otsu Method Integrated with Edge Detection and Decision Tree for Crack Detection in Highway Transportation Infrastructure |
| title_short | An Advanced Otsu Method Integrated with Edge Detection and Decision Tree for Crack Detection in Highway Transportation Infrastructure |
| title_sort | advanced otsu method integrated with edge detection and decision tree for crack detection in highway transportation infrastructure |
| url | http://dx.doi.org/10.1155/2021/9205509 |
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