Dynamic Pavement Distress Image Stitching Based on Fine-Grained Feature Matching
Camera-based pavement distress detection plays an important role in pavement maintenance. Duplicate collections for the same distress and multiple overlaps of defects are both practical problems that greatly affect the detection results. In this paper, we propose a fine-grained feature-matching and...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/5804835 |
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| _version_ | 1832553299080904704 |
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| author | Yuchuan Du Zihang Weng Chenglong Liu Difei Wu |
| author_facet | Yuchuan Du Zihang Weng Chenglong Liu Difei Wu |
| author_sort | Yuchuan Du |
| collection | DOAJ |
| description | Camera-based pavement distress detection plays an important role in pavement maintenance. Duplicate collections for the same distress and multiple overlaps of defects are both practical problems that greatly affect the detection results. In this paper, we propose a fine-grained feature-matching and image-stitching method for pavement distress detection to eliminate duplications and visually demonstrates local pavement distress. The original images are processed through a hierarchical structure, including rough data filtering, feature matching, and image stitching. The original data are firstly filtered based on the global position system (GPS) information, which can avoid full-dataset comparison and improve the calculating efficiency. A scale-invariant feature transform is introduced for feature matching based on the extracted key regions using spectral saliency mapping and bounding boxes. Two parameters: the mean Euclidean distance (MEuD) and the matching rate (MCR) are constructed to identify the duplication between two images. A support vector machine is then applied to determine the threshold of MEuD and MCR. This paper further discusses the correlation between the sampling frequency and the number of detection vehicles. The method provided can effectively solve the problem of duplications in pavement distress detection and enhances the feasibility of multivehicle pavement distress detection based on images. |
| format | Article |
| id | doaj-art-e2a966d2f2ad4c5785de51f37b461476 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-e2a966d2f2ad4c5785de51f37b4614762025-02-03T05:54:27ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/58048355804835Dynamic Pavement Distress Image Stitching Based on Fine-Grained Feature MatchingYuchuan Du0Zihang Weng1Chenglong Liu2Difei Wu3The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, ChinaThe Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, ChinaCamera-based pavement distress detection plays an important role in pavement maintenance. Duplicate collections for the same distress and multiple overlaps of defects are both practical problems that greatly affect the detection results. In this paper, we propose a fine-grained feature-matching and image-stitching method for pavement distress detection to eliminate duplications and visually demonstrates local pavement distress. The original images are processed through a hierarchical structure, including rough data filtering, feature matching, and image stitching. The original data are firstly filtered based on the global position system (GPS) information, which can avoid full-dataset comparison and improve the calculating efficiency. A scale-invariant feature transform is introduced for feature matching based on the extracted key regions using spectral saliency mapping and bounding boxes. Two parameters: the mean Euclidean distance (MEuD) and the matching rate (MCR) are constructed to identify the duplication between two images. A support vector machine is then applied to determine the threshold of MEuD and MCR. This paper further discusses the correlation between the sampling frequency and the number of detection vehicles. The method provided can effectively solve the problem of duplications in pavement distress detection and enhances the feasibility of multivehicle pavement distress detection based on images.http://dx.doi.org/10.1155/2020/5804835 |
| spellingShingle | Yuchuan Du Zihang Weng Chenglong Liu Difei Wu Dynamic Pavement Distress Image Stitching Based on Fine-Grained Feature Matching Journal of Advanced Transportation |
| title | Dynamic Pavement Distress Image Stitching Based on Fine-Grained Feature Matching |
| title_full | Dynamic Pavement Distress Image Stitching Based on Fine-Grained Feature Matching |
| title_fullStr | Dynamic Pavement Distress Image Stitching Based on Fine-Grained Feature Matching |
| title_full_unstemmed | Dynamic Pavement Distress Image Stitching Based on Fine-Grained Feature Matching |
| title_short | Dynamic Pavement Distress Image Stitching Based on Fine-Grained Feature Matching |
| title_sort | dynamic pavement distress image stitching based on fine grained feature matching |
| url | http://dx.doi.org/10.1155/2020/5804835 |
| work_keys_str_mv | AT yuchuandu dynamicpavementdistressimagestitchingbasedonfinegrainedfeaturematching AT zihangweng dynamicpavementdistressimagestitchingbasedonfinegrainedfeaturematching AT chenglongliu dynamicpavementdistressimagestitchingbasedonfinegrainedfeaturematching AT difeiwu dynamicpavementdistressimagestitchingbasedonfinegrainedfeaturematching |