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dc.contributor.authorHadjidemetriou, Georgios M.en
dc.contributor.authorChristodoulou, Symeon E.en
dc.contributor.authorVela, Patricio A.en
dc.creatorHadjidemetriou, Georgios M.en
dc.creatorChristodoulou, Symeon E.en
dc.creatorVela, Patricio A.en
dc.date.accessioned2019-04-18T06:19:08Z
dc.date.available2019-04-18T06:19:08Z
dc.date.issued2016
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/45510
dc.description.abstractThe efficient condition assessment of road networks is crucial to prevent pavement distresses which can cause a spectrum of detrimental effects. The need for automation of the underlying process is originated from the costly, time-consuming and dangerous current methods. Presented herein is the automation of the patch detection process, which is essential for pavement surface evaluation and rating. The method is based on Support Vector Machine (SVM) Classification. The road pavement images are divided into square blocks and the SVM is trained and tested by feature vectors generated from these blocks. The feature vectors consist of the histogram and two texture descriptors, using the discrete cosine transform (DCT) and the Gray-Level Co-Occurrence Matrix (GLCM). The output is a binary image, where each image block is classified as "patch" or "no-patch". The performance of the proposed MatlabTM implementation, which uses data collected from real-life urban networks, is rated by a detection accuracy of 81.97 %, a precision of 64.21 %, and a recall of 91.21 %.en
dc.language.isoengen
dc.sourceThe Institute of Electrical and Electronics Engineers, Inc.(IEEE) Conference Proceedings.en
dc.source.urihttp://search.proquest.com/docview/1825523760?accountid=17200
dc.subjectMathematical analysisen
dc.subjectAutomationen
dc.subjectClassificationen
dc.subject90: Electronics and Communications Milieux (General) (EA)en
dc.subjectElectronics and Communications Abstracts (EA)en
dc.subjectImagesen
dc.subjectPavementsen
dc.subjectRoadsen
dc.subjectSupport vector machinesen
dc.subjectTextureen
dc.titleAutomated detection of pavement patches utilizing support vector machine classificationen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doihttp://dx.doi.org/10.1109/MELCON.2016.7495460
dc.description.startingpage1
dc.description.endingpage5
dc.author.facultyΠολυτεχνική Σχολή / Faculty of Engineering
dc.author.departmentΤμήμα Πολιτικών Μηχανικών και Μηχανικών Περιβάλλοντος / Department of Civil and Environmental Engineering
dc.type.uhtypeArticleen
dc.contributor.orcidChristodoulou, Symeon E. [0000-0002-9859-0381]


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