A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Beamlet Transform‐Based Technique for Pavement Crack Detection and Classification
Abstract: 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.
Beamlet Transform‐Based Technique for Pavement Crack Detection and Classification
Abstract: 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.
Beamlet Transform‐Based Technique for Pavement Crack Detection and Classification
Ying, L. (author) / Salari, E. (author)
Computer‐Aided Civil and Infrastructure Engineering ; 25 ; 572-580
2010-11-01
9 pages
Article (Journal)
Electronic Resource
English
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