Road Surface State Recognition Based on SVM Optimization and Image Segmentation Processing
Adverse road condition is the main cause of traffic accidents. Road surface condition recognition based on video image has become a central issue. However, hybrid road surface and road surface under different lighting environments are two crucial problems. In this paper, the road surface states are...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2017/6458495 |
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| _version_ | 1832551635058950144 |
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| author | Jiandong Zhao Hongqiang Wu Liangliang Chen |
| author_facet | Jiandong Zhao Hongqiang Wu Liangliang Chen |
| author_sort | Jiandong Zhao |
| collection | DOAJ |
| description | Adverse road condition is the main cause of traffic accidents. Road surface condition recognition based on video image has become a central issue. However, hybrid road surface and road surface under different lighting environments are two crucial problems. In this paper, the road surface states are categorized into 5 types including dry, wet, snow, ice, and water. Then, according to the original image size, images are segmented; 9-dimensional color eigenvectors and 4 texture eigenvectors are extracted to construct road surface state characteristics database. Next, a recognition method of road surface state based on SVM (Support Vector Machine) is proposed. In order to improve the recognition accuracy and the universality, a grid searching algorithm and PSO (Particle Swarm Optimization) algorithm are used to optimize the kernel function factor and penalty factor of SVM. Finally, a large number of actual road surface images in different environments are tested. The results show that the method based on SVM and image segmentation is feasible. The accuracy of PSO algorithm is more than 90%, which effectively solves the problem of road surface state recognition under the condition of hybrid or different video scenes. |
| format | Article |
| id | doaj-art-d4f9e4362f4e4824bfa2250cafb6ad1f |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-d4f9e4362f4e4824bfa2250cafb6ad1f2025-02-03T06:01:04ZengWileyJournal of Advanced Transportation0197-67292042-31952017-01-01201710.1155/2017/64584956458495Road Surface State Recognition Based on SVM Optimization and Image Segmentation ProcessingJiandong Zhao0Hongqiang Wu1Liangliang Chen2School of Mechanical and Electronic Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Mechanical and Electronic Control Engineering, Beijing Jiaotong University, Beijing 100044, ChinaNational Engineering Laboratory for Surface Transportation Weather Impacts Prevention, Broadvision Engineering Consultants, Kunming 650041, ChinaAdverse road condition is the main cause of traffic accidents. Road surface condition recognition based on video image has become a central issue. However, hybrid road surface and road surface under different lighting environments are two crucial problems. In this paper, the road surface states are categorized into 5 types including dry, wet, snow, ice, and water. Then, according to the original image size, images are segmented; 9-dimensional color eigenvectors and 4 texture eigenvectors are extracted to construct road surface state characteristics database. Next, a recognition method of road surface state based on SVM (Support Vector Machine) is proposed. In order to improve the recognition accuracy and the universality, a grid searching algorithm and PSO (Particle Swarm Optimization) algorithm are used to optimize the kernel function factor and penalty factor of SVM. Finally, a large number of actual road surface images in different environments are tested. The results show that the method based on SVM and image segmentation is feasible. The accuracy of PSO algorithm is more than 90%, which effectively solves the problem of road surface state recognition under the condition of hybrid or different video scenes.http://dx.doi.org/10.1155/2017/6458495 |
| spellingShingle | Jiandong Zhao Hongqiang Wu Liangliang Chen Road Surface State Recognition Based on SVM Optimization and Image Segmentation Processing Journal of Advanced Transportation |
| title | Road Surface State Recognition Based on SVM Optimization and Image Segmentation Processing |
| title_full | Road Surface State Recognition Based on SVM Optimization and Image Segmentation Processing |
| title_fullStr | Road Surface State Recognition Based on SVM Optimization and Image Segmentation Processing |
| title_full_unstemmed | Road Surface State Recognition Based on SVM Optimization and Image Segmentation Processing |
| title_short | Road Surface State Recognition Based on SVM Optimization and Image Segmentation Processing |
| title_sort | road surface state recognition based on svm optimization and image segmentation processing |
| url | http://dx.doi.org/10.1155/2017/6458495 |
| work_keys_str_mv | AT jiandongzhao roadsurfacestaterecognitionbasedonsvmoptimizationandimagesegmentationprocessing AT hongqiangwu roadsurfacestaterecognitionbasedonsvmoptimizationandimagesegmentationprocessing AT liangliangchen roadsurfacestaterecognitionbasedonsvmoptimizationandimagesegmentationprocessing |