Computational Technology Resources - CCP

G. Hadjidemetriou¹, M.M. Serrano², P.A. Vela² and S. Christodoulou¹

¹Department of Civil and Environmental Engineering, University of Cyprus, Nicosia, Cyprus
²Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, United States of America

doi:10.4203/ccp.108.283

purchase the full-text of this paper

G. Hadjidemetriou, M.M. Serrano, P.A. Vela, S. Christodoulou, "Patch Defects in Images using Support Vector Machines", in J. Kruis, Y. Tsompanakis, B.H.V. Topping, (Editors), "Proceedings of the Fifteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Stirlingshire, UK, Paper 283, 2015. doi:10.4203/ccp.108.283

Keywords: pavement condition assessment, patch detection, computer vision, image processing, support vector machine, MATLAB.

Summary

Condition assessment of roadway transport networks evaluates the vulnerability of road segments and by extension the future reliability of such networks. Traditional road pavement condition assessment involves manual effort, which requires training, is costly and time-consuming given the quantity of road needing assessment, and poses a risk to the safety of inspectors involved. Automated roadway assessment methods using computer vision can overcome these limitations. In this paper, the detection of patch type distresses in images, taken from video cameras positioned on vehicles is presented. This method is based on support vector machine (SVM) classification applied to the image data. The process first divides a test image into square blocks, then generates a feature vector from the block to be used in the SVM. Two feature vector types are tested: histogram and texture using the discrete cosine transform. The result is an output with each image block being classified as "patch" or "no-patch". The recognition results of the case study from a real-life urban network are promising, showing a detection accuracy of about 81%, based on a MatlabTM implementation

purchase the full-text of this paper (price £20)

go to the previous paper
go to the next paper
return to the table of contents
return to the book description
purchase this book (price £75 +P&P)

	Computational & Technology Resources an online resource for computational, engineering & technology publications
	not logged in - login
Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 108 PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING Edited by: J. Kruis, Y. Tsompanakis and B.H.V. Topping Paper 283 Patch Defects in Images using Support Vector Machines G. Hadjidemetriou¹, M.M. Serrano², P.A. Vela² and S. Christodoulou¹ ¹Department of Civil and Environmental Engineering, University of Cyprus, Nicosia, Cyprus ²Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, United States of America doi:10.4203/ccp.108.283 purchase the full-text of this paper Full Bibliographic Reference for this paper G. Hadjidemetriou, M.M. Serrano, P.A. Vela, S. Christodoulou, "Patch Defects in Images using Support Vector Machines", in J. Kruis, Y. Tsompanakis, B.H.V. Topping, (Editors), "Proceedings of the Fifteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Stirlingshire, UK, Paper 283, 2015. doi:10.4203/ccp.108.283 Keywords: pavement condition assessment, patch detection, computer vision, image processing, support vector machine, MATLAB. Summary Condition assessment of roadway transport networks evaluates the vulnerability of road segments and by extension the future reliability of such networks. Traditional road pavement condition assessment involves manual effort, which requires training, is costly and time-consuming given the quantity of road needing assessment, and poses a risk to the safety of inspectors involved. Automated roadway assessment methods using computer vision can overcome these limitations. In this paper, the detection of patch type distresses in images, taken from video cameras positioned on vehicles is presented. This method is based on support vector machine (SVM) classification applied to the image data. The process first divides a test image into square blocks, then generates a feature vector from the block to be used in the SVM. Two feature vector types are tested: histogram and texture using the discrete cosine transform. The result is an output with each image block being classified as "patch" or "no-patch". The recognition results of the case study from a real-life urban network are promising, showing a detection accuracy of about 81%, based on a MatlabTM implementation purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £75 +P&P)
Back to top	©Civil-Comp Limited 2023 - terms & conditions