Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images
Pavement cracking is a significant symptom of pavement deterioration and deficiency. Conventional manual inspections of road condition are gradually replaced by novel automated inspection systems. As a result, a great amount of pavement surface information is digitized by these systems with a high r...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/1813763 |
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| _version_ | 1832566552990318592 |
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| author | Baoxian Li Kelvin C. P. Wang Allen Zhang Yue Fei |
| author_facet | Baoxian Li Kelvin C. P. Wang Allen Zhang Yue Fei |
| author_sort | Baoxian Li |
| collection | DOAJ |
| description | Pavement cracking is a significant symptom of pavement deterioration and deficiency. Conventional manual inspections of road condition are gradually replaced by novel automated inspection systems. As a result, a great amount of pavement surface information is digitized by these systems with a high resolution. With pavement surface data, pavement cracks can be detected using crack detection algorithms. In this paper, a fully automated algorithm for segmenting and enhancing pavement crack is proposed, which consists of four major procedures. First, a preprocessing procedure is employed to remove spurious noise and rectify the original 3D pavement data. Second, crack saliency maps are segmented from 3D pavement data using steerable matched filter bank. Third, 2D tensor voting is applied to crack saliency maps to achieve better curve continuity of crack structure and higher accuracy. Finally, postprocessing procedures are used to remove redundant noises. The proposed procedures were evaluated over 200 asphalt pavement images with diverse cracks. The experimental results demonstrated that the proposed method showed a high performance and could achieve average precision of 88.38%, recall of 93.15%, and F-measure of 90.68%, respectively. Accordingly, the proposed approach can be helpful in automated pavement condition assessment. |
| format | Article |
| id | doaj-art-94ecb9c8f4c84b888306041eb76bd590 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-94ecb9c8f4c84b888306041eb76bd5902025-02-03T01:03:44ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/18137631813763Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement ImagesBaoxian Li0Kelvin C. P. Wang1Allen Zhang2Yue Fei3School of Civil and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Civil and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Civil and Environmental Engineering, Oklahoma State University, Stillwater, OK 74078, USASchool of Civil and Environmental Engineering, Oklahoma State University, Stillwater, OK 74078, USAPavement cracking is a significant symptom of pavement deterioration and deficiency. Conventional manual inspections of road condition are gradually replaced by novel automated inspection systems. As a result, a great amount of pavement surface information is digitized by these systems with a high resolution. With pavement surface data, pavement cracks can be detected using crack detection algorithms. In this paper, a fully automated algorithm for segmenting and enhancing pavement crack is proposed, which consists of four major procedures. First, a preprocessing procedure is employed to remove spurious noise and rectify the original 3D pavement data. Second, crack saliency maps are segmented from 3D pavement data using steerable matched filter bank. Third, 2D tensor voting is applied to crack saliency maps to achieve better curve continuity of crack structure and higher accuracy. Finally, postprocessing procedures are used to remove redundant noises. The proposed procedures were evaluated over 200 asphalt pavement images with diverse cracks. The experimental results demonstrated that the proposed method showed a high performance and could achieve average precision of 88.38%, recall of 93.15%, and F-measure of 90.68%, respectively. Accordingly, the proposed approach can be helpful in automated pavement condition assessment.http://dx.doi.org/10.1155/2019/1813763 |
| spellingShingle | Baoxian Li Kelvin C. P. Wang Allen Zhang Yue Fei Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images Journal of Advanced Transportation |
| title | Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images |
| title_full | Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images |
| title_fullStr | Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images |
| title_full_unstemmed | Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images |
| title_short | Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images |
| title_sort | automatic segmentation and enhancement of pavement cracks based on 3d pavement images |
| url | http://dx.doi.org/10.1155/2019/1813763 |
| work_keys_str_mv | AT baoxianli automaticsegmentationandenhancementofpavementcracksbasedon3dpavementimages AT kelvincpwang automaticsegmentationandenhancementofpavementcracksbasedon3dpavementimages AT allenzhang automaticsegmentationandenhancementofpavementcracksbasedon3dpavementimages AT yuefei automaticsegmentationandenhancementofpavementcracksbasedon3dpavementimages |