Premium
An improved algorithm for image crack detection based on percolation model
Author(s) -
Qu Zhong,
Lin LiDan,
Guo Yang,
Wang Ning
Publication year - 2015
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22056
Subject(s) - algorithm , percolation (cognitive psychology) , brightness , image (mathematics) , noise (video) , noise reduction , computer science , feature (linguistics) , image processing , artificial intelligence , image denoising , computer vision , physics , optics , linguistics , philosophy , biology , neuroscience
In a complex background, because of uneven illumination, concrete bubbles, shadows of various shapes, and other noise, traditional crack detection methods based on image processing cannot accurately detect cracks, especially unclear cracks. A crack detection method based on the percolation model fully considers the features of cracks including the characteristics of brightness and length, and therefore can accurately detect cracks in the image. But this method is time consuming, and some noisy areas are detected as crack regions. In order to solve the problems above, we propose an improved algorithm for image crack detection, which includes an accelerated algorithm and a new denoising method based on the percolation model. The accelerated algorithm decreases the number of iterations of percolation processing to reduce the computing time, and the denoising method is based on the characteristic of brightness and the length feature of cracks to remove the noisy regions. Experimental results show that the proposed algorithm can accurately and efficiently detect cracks in the image. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.