Crack image detection based on fractional differential and fractal dimension. Issue 1 (10th January 2019)

• Record Type: Journal Article
• Authors: Cao, Ting; Wang, Weixing; Tighe, Susan; Wang, Shenglin
• Is Part Of: IET computer vision. Volume 13:Issue 1(2019)
• Page Start: 79
• Page End: 85
• Publication Date: 2019-01-10
• Subjects: feature extraction -- fractals -- image texture -- crack detection -- image enhancement -- road building -- civil engineering computing -- maintenance engineering; crack image detection -- fractal dimension -- image processing -- crack detection method -- fractional differential dimension -- crack extraction -- image enhancement algorithm -- fuzzy crack boundary -- crack boundary information -- extraction algorithm -- pavement maintenance systems -- texture details; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2018.5337
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : 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.