Improved methods for classification of pavement distress images

Koutsopoulos, HN; Kapotis, VI; Downey, AB

dc.contributor.author	Koutsopoulos, HN	en
dc.contributor.author	Kapotis, VI	en
dc.contributor.author	Downey, AB	en
dc.date.accessioned	2014-03-01T01:42:37Z
dc.date.available	2014-03-01T01:42:37Z
dc.date.issued	1994	en
dc.identifier.issn	0968090X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/23877
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-40649100743&partnerID=40&md5=48b110721da142e20f33defc7d4d25f1	en
dc.title	Improved methods for classification of pavement distress images	en
heal.type	journalArticle	en
heal.publicationDate	1994	en
heal.abstract	Methods for automated classification of pavement distress images are examined and compared. Images are divided into regions, and a two-stage classification procedure takes place. First, the regions are classified into primitives (plain, longitudinal, transverse, diagonal, or joint), which are the building blocks characterizing the various distress classes. The regional classification results are aggregated and used as input for the classification of the entire image to one of the classes of interest (plain, longitudinal, transverse, block, or alligator). A large number of features are examined using discriminant analysis, k-Nearest Neighbor, and discrete choice models. Conclusions are drawn on the discriminatory power of the various features and the appropriateness of the classification methods. For both levels, the logit model provided the best and most robust classification results. The sensitivity of the overall classification accuracy to the accuracy of primitive classification is also investigated. © 1994.	en
heal.journalName	Transportation Research Part C	en
dc.identifier.volume	2	en
dc.identifier.issue	1	en
dc.identifier.spage	19	en
dc.identifier.epage	33	en