TY - GEN
T1 - A novel automatic concrete surface crack identification using isotropic undecimated wavelet transform
AU - Nguyen, Hoang Nam
AU - Kam, Tai-Yan
AU - Cheng, Pi-Ying
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Traditionally, concrete surface cracks are manually measured and recorded by experienced inspectors who observe cracks with their naked eye. This task is very costly, time-consuming and dangerous. Some automatic crack detection techniques utilizing image processing have been proposed. However, it is very difficult to automatically identify cracks sufficiently fast and accurate. This paper presents a novel automatic algorithm for effectively identifying cracks in concrete structures, which can be applied to high resolution photographs. Firstly, as cracks can be considered as elongated dark objects in concrete surface images, all dark objects are roughly identified using Isotropic Undecimated Wavelet Transform (IUWT) and morphological image processing. Secondly, all previous detected dark objects are classified into crack and non-crack objects by analyzing their cross section pixel intensity profiles, which are computed perpendicularly across their centre-lines in the original input image, using a novel method based on Savitzky-Golay filter. Therefore, both local and global changes in crack width can be readily quantified. Experiments show that our method can automatically detect true cracks in concrete surface images using a unique set of parameters.
AB - Traditionally, concrete surface cracks are manually measured and recorded by experienced inspectors who observe cracks with their naked eye. This task is very costly, time-consuming and dangerous. Some automatic crack detection techniques utilizing image processing have been proposed. However, it is very difficult to automatically identify cracks sufficiently fast and accurate. This paper presents a novel automatic algorithm for effectively identifying cracks in concrete structures, which can be applied to high resolution photographs. Firstly, as cracks can be considered as elongated dark objects in concrete surface images, all dark objects are roughly identified using Isotropic Undecimated Wavelet Transform (IUWT) and morphological image processing. Secondly, all previous detected dark objects are classified into crack and non-crack objects by analyzing their cross section pixel intensity profiles, which are computed perpendicularly across their centre-lines in the original input image, using a novel method based on Savitzky-Golay filter. Therefore, both local and global changes in crack width can be readily quantified. Experiments show that our method can automatically detect true cracks in concrete surface images using a unique set of parameters.
KW - Savitzky-Golay filter
KW - crack detection
KW - thinning algorithm
KW - wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=84875684599&partnerID=8YFLogxK
U2 - 10.1109/ISPACS.2012.6473594
DO - 10.1109/ISPACS.2012.6473594
M3 - Conference contribution
AN - SCOPUS:84875684599
SN - 9781467350815
T3 - ISPACS 2012 - IEEE International Symposium on Intelligent Signal Processing and Communications Systems
SP - 766
EP - 771
BT - ISPACS 2012 - IEEE International Symposium on Intelligent Signal Processing and Communications Systems
Y2 - 4 November 2012 through 7 November 2012
ER -