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Beamlet Transform‐Based Technique for Pavement Crack Detection and Classification
Author(s) -
Ying L.,
Salari E.
Publication year - 2010
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.2010.00674.x
Subject(s) - computer science , block (permutation group theory) , artificial intelligence , image (mathematics) , multiplicative function , computer vision , pattern recognition (psychology) , mathematics , geometry , mathematical analysis
This article presents a Beamlet transform‐based approach to automatically detect and classify pavement cracks in digital images. The proposed method uses a pavement distress image enhancement algorithm to correct the nonuniform background illumination by calculating the multiplicative factors that eliminate the background lighting variation. To extract linear features such as surface cracks from the pavement images, the image is partitioned into small windows and a Beamlet transform‐based algorithm is applied. The crack segments are then linked together and classified into four types: vertical, horizontal, transversal, and block. Simulation results show that the method is effective and robust in the extraction of cracks on a variety of pavement images .