Crack image detection based on fractional differential and fractal dimension

被引:29
作者
Cao, Ting [1 ,2 ]
Wang, Weixing [2 ]
Tighe, Susan [2 ,3 ]
Wang, Shenglin [3 ]
机构
[1] Xian Univ Technol, Sch Comp Sci & Engn, 5 Jinhua Rd South, Xian, Shaanxi, Peoples R China
[2] Changan Univ, Sch Informat Engn, Middle Sect, Nanerhuan Rd, Xian, Shaanxi, Peoples R China
[3] Univ Waterloo, Dept Civil & Environm Engn, 200 Univ Ave West, Waterloo, ON, Canada
关键词
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;
D O I
10.1049/iet-cvi.2018.5337
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:79 / 85
页数:7
相关论文
共 27 条
[1]   Depth Image Enhancement and Detection on NSCT and Fractional Differential [J].
Cao, Ting ;
Wang, Weixing .
WIRELESS PERSONAL COMMUNICATIONS, 2018, 103 (01) :1025-1035
[2]   A new IIR-type digital fractional order differentiator [J].
Chen, YQ ;
Vinagre, BM .
SIGNAL PROCESSING, 2003, 83 (11) :2359-2365
[3]   An adaptive approach for texture enhancement based on a fractional differential operator with non-integer step and order [J].
Hu, Fuyuan ;
Si, Shaohui ;
Wong, Hau San ;
Fu, Baochuan ;
Si, MaoXin ;
Luo, Heng .
NEUROCOMPUTING, 2015, 158 :295-306
[4]  
Huang G., 2012, IEEE INT C COMP SCI
[5]   Adaptive fractional differential approach and its application to medical image enhancement [J].
Li, Bo ;
Xie, Wei .
COMPUTERS & ELECTRICAL ENGINEERING, 2015, 45 :324-335
[6]  
Li Gang, 2010, Computer Engineering and Applications, V46, P224, DOI 10.3778/j.issn.1002-8331.2010.01.067
[7]   Novel approach to pavement image segmentation based on neighboring difference histogram method [J].
Li Qingquan ;
Liu Xianglong .
CISP 2008: FIRST INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, VOL 2, PROCEEDINGS, 2008, :792-796
[8]   FoSA: F* Seed-growing Approach for crack-line detection from pavement images [J].
Li, Qingquan ;
Zou, Qin ;
Zhang, Daqiang ;
Mao, Qingzhou .
IMAGE AND VISION COMPUTING, 2011, 29 (12) :861-872
[9]   Novel Approach to Pavement Cracking Automatic Detection Based on Segment Extending [J].
Liu, Fanfan ;
Xu, Guoai ;
Yang, Yixian ;
Niu, Xinxin ;
Pan, Yuli .
KAM: 2008 INTERNATIONAL SYMPOSIUM ON KNOWLEDGE ACQUISITION AND MODELING, PROCEEDINGS, 2008, :610-614
[10]  
[刘晟 Liu Sheng], 2015, [长安大学学报. 自然科学版, Journal of Chang'An University. Natural Science Edition], V35, P13