Open AccessCrack image detection based on fractional differential and fractal dimension
Author(s) -
Cao Ting,
Wang Weixing,
Tighe Susan,
Wang Shenglin
Publication year - 2019
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2018.5337
Subject(s) - fractal dimension , boundary (topology) , fractal , dimension (graph theory) , feature extraction , artificial intelligence , fuzzy logic , computer science , image (mathematics) , mathematics , computer vision , algorithm , mathematical analysis , pure mathematics
In civil engineering, crack detection using image processing has gained much attention among researchers and transportation agencies. As the crack image often presents a fuzzy boundary and random shape, it is difficult to achieve satisfactory detection performance. This study proposes a crack detection method based on the fractional differential and fractal dimension. This method achieves image enhancement and crack extraction in two stages. First, an image enhancement algorithm based on the fractional differential is applied to solve the fuzzy crack boundary. This algorithm can enhance the crack boundary information significantly while simultaneously maintaining texture details. Second, an improved extraction algorithm based on the fractal dimension is studied. This algorithm can effectively accomplish crack extraction according to shape features. Last, upon comparisons with classic and state‐of‐the‐art methods, the experiment shows that the proposed method can achieve satisfactory results for crack image detection.