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04468aam a2200529Ic 4500 001 F9640EDCB7CA11EC864CB83724ECA4DB 003 SILO 005 20220409010100 007 cu c|||||||||| 008 160405s2016 iauad fsbt s000 0 eng d 035 $a (OCoLC)945989477 040 $a UIG $b eng $e rda $c UIG $d SILO 100 1 $a Gopalakrishnan, Kasthurira, $e author. $u Iowa State University. 245 10 $a Machine-Vision-Based Roadway Health Monitoring and Assessment : $b Development of a Shape-Based Pavement-Crack-Detection Approach $c Kasthurirangan Gopalakrishnan, Arun Somani, Omar Smadi, Teng Wang, Halil Ceylan 264 1 $a Ames, Iowa $b Institute for Transportation, Iowa State University $c 2016 300 $a 34 pages ( 49 pages in PDF file) $b illustrations, charts (color) 500 $a "Part of DTRT13-G-UTC37 500 $a January 2016 -- Technical Report Documentation Page 504 $a Includes bibliographic references (pages 33- 34) 513 $a Final Report 520 3 $a State highway agencies (SHAs) routinely employ semi-automated and automated image-based methods for network-level pavement-cracking data collection, and there are different types of pavement-cracking data collected by SHAs for reporting and management purposes. The main objective of this proof-of-concept research was to develop a shape-based pavement-crack-detection approach for the reliable detection and classification of cracks from acquired two-dimensional (2D) concrete and asphalt pavement surface images. The developed pavement-crack-detection algorithm consists of four stages: local filtering, maximum component extraction, polynomial fitting of possible crack pixels, and shape metric computation and filtering. After completing the crack-detection process, the width of each crack segment is computed to classify the cracks. In order to verify the developed crack-detection approach, a series of experiments was conducted on real pavement images without and with cracks at different severities. The developed shape-based pavement crack detection algorithm was able to detect cracks at different severities from both asphalt and concrete pavement images. Further, the developed algorithm was able to compute crack widths from the images for crack classification and reporting purposes. Additional research is needed to improve the robustness and accuracy of the developed approach in the presence of anomalies and other surface irregularities. 500 $a Catalogers note: This record is for the full-length report. A 2-page tech transfer summary is also available, at a link in this record. The summary is not cataloged individually. 516 $a Text file in PDF format. 536 $a Performed by Iowa State University, Institute for Transportation 536 $a Sponsored by the Midwest Transportation Center 536 $a Sponsored by the U.S. Department of Transportation 538 $a System requirements: Adobe Acrobat, Adobe Acrobat Reader, or other PDF reading software. 538 $a Mode of access: World Wide Web $u http://www.intrans.iastate.edu/research/projects/detail/?projectID=72765752 $u http://www.intrans.iastate.edu/research/projects/detail/?projectID=72765752 650 0 $a Pavements, Concrete $x Cracking $z Iowa. 650 07 $a Algorithms $2 trt 650 07 $a Asphalt pavements $2 trt 650 07 $a Data collection $2 trt 650 07 $a Image analysis $2 trt 650 07 $a Pavement cracking $2 trt 700 1 $a Somani, Arun, $e author. $u Iowa State University. 700 1 $a Smadi, Omar, $e author. $u Iowa State University. 700 1 $a Wang, Teng, $e author. $u Iowa State University. 700 1 $a Ceylan, Halil, $e author. $u Iowa State University. 710 2 $a Iowa State University. $b Institute for Transportation, $e performing body. 710 1 $a Iowa. $b Midwest Transportation Center, $e sponsoring body. 710 1 $a United States. $b Department of Transportation, $e sponsoring body. 856 40 $a Digital Repository @ Iowa State University $3 Full text access from Digital Repository @ Iowa State University $z Full report $u http://lib.dr.iastate.edu/intrans_reports/189 856 42 $a Digital Repository @ Iowa State University $3 Full text access from Digital Repository @ Iowa State University $z Tech transfer summary $u http://lib.dr.iastate.edu/intrans_techtransfer/102 941 $a 1 952 $l IAOX771 $d 20240710103205.0 956 $a http://locator.silo.lib.ia.us/search.cgi?index_0=id&term_0=F9640EDCB7CA11EC864CB83724ECA4DB 994 $a C0 $b UIGInitiate Another SILO Locator Search