Adaptive Road Crack Detection System by Pavement Classification
This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2011-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/11/10/9628/ |
| _version_ | 1818036509007151104 |
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| author | Alejandro Amírola Pedro Yarza Pedro Aliseda Manuel Ocaña Ignacio Parra Miguel A. Sotelo David F. Llorca Miguel Gavilán Oscar Marcos David Balcones |
| author_facet | Alejandro Amírola Pedro Yarza Pedro Aliseda Manuel Ocaña Ignacio Parra Miguel A. Sotelo David F. Llorca Miguel Gavilán Oscar Marcos David Balcones |
| author_sort | Alejandro Amírola |
| collection | DOAJ |
| description | This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement. |
| first_indexed | 2024-12-10T07:12:04Z |
| format | Article |
| id | doaj.art-504139abeef44b5eaefbe1f99a3383b4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T07:12:04Z |
| publishDate | 2011-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-504139abeef44b5eaefbe1f99a3383b42022-12-22T01:58:01ZengMDPI AGSensors1424-82202011-10-0111109628965710.3390/s111009628Adaptive Road Crack Detection System by Pavement ClassificationAlejandro AmírolaPedro YarzaPedro AlisedaManuel OcañaIgnacio ParraMiguel A. SoteloDavid F. LlorcaMiguel GavilánOscar MarcosDavid BalconesThis paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement.http://www.mdpi.com/1424-8220/11/10/9628/road distress detectionroad surface classificationlinear featuresmulti-class SVMlocal binary patterngray-level co-occurrence matrix |
| spellingShingle | Alejandro Amírola Pedro Yarza Pedro Aliseda Manuel Ocaña Ignacio Parra Miguel A. Sotelo David F. Llorca Miguel Gavilán Oscar Marcos David Balcones Adaptive Road Crack Detection System by Pavement Classification Sensors road distress detection road surface classification linear features multi-class SVM local binary pattern gray-level co-occurrence matrix |
| title | Adaptive Road Crack Detection System by Pavement Classification |
| title_full | Adaptive Road Crack Detection System by Pavement Classification |
| title_fullStr | Adaptive Road Crack Detection System by Pavement Classification |
| title_full_unstemmed | Adaptive Road Crack Detection System by Pavement Classification |
| title_short | Adaptive Road Crack Detection System by Pavement Classification |
| title_sort | adaptive road crack detection system by pavement classification |
| topic | road distress detection road surface classification linear features multi-class SVM local binary pattern gray-level co-occurrence matrix |
| url | http://www.mdpi.com/1424-8220/11/10/9628/ |
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