Adaptive Road Crack Detection System by Pavement Classification
| Autor: | Pedro Yarza, Miguel Angel Sotelo, David Balcones, Pedro Aliseda, Manuel Ocaña, Óscar Marcos, David Fernández Llorca, Alejandro Amírola, I. Parra, M. Gavilan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Engineering
Surface Properties Local binary patterns Feature vector Poison control Transportation lcsh:Chemical technology Biochemistry Article Analytical Chemistry gray-level co-occurrence matrix Digital image road distress detection Image Processing Computer-Assisted False positive paradox lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation linear features Simulation local binary pattern Construction Materials business.industry Reproducibility of Results Pattern recognition road surface classification multi-class SVM Atomic and Molecular Physics and Optics Motor Vehicles Laser illumination Stress Mechanical Artificial intelligence Structural health monitoring business Classifier (UML) Algorithms |
| Zdroj: | Sensors, Vol 11, Iss 10, Pp 9628-9657 (2011) Sensors; Volume 11; Issue 10; Pages: 9628-9657 Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|