Volume 22 Issue 2
Apr.  2022
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YIN Guan-sheng, GAO Jian-guo, SHI Ming-hui, JIN Ming-zhu, TUO Hong-liang, LI Chang, ZHANG Bo. Tunnel crack recognition method under image block[J]. Journal of Traffic and Transportation Engineering, 2022, 22(2): 148-159. doi: 10.19818/j.cnki.1671-1637.2022.02.011
Citation: YIN Guan-sheng, GAO Jian-guo, SHI Ming-hui, JIN Ming-zhu, TUO Hong-liang, LI Chang, ZHANG Bo. Tunnel crack recognition method under image block[J]. Journal of Traffic and Transportation Engineering, 2022, 22(2): 148-159. doi: 10.19818/j.cnki.1671-1637.2022.02.011
shu

Tunnel crack recognition method under image block

doi: 10.19818/j.cnki.1671-1637.2022.02.011

  • School of Sciences, Chang'an University, Xi'an 710064, Shaanxi, China

Funds:

National Natural Science Foundation of China 11702033

Science and Technology Project of Department of Transport of Shaanxi Province 13-16K

Scientific Innovation Practice Project of Postgraduates of Chang'an University 300103714017

More Information
  • Author Bio:

    YIN Guan-sheng(1958-), male, professor, PhD, yings@chd.edu.cn

  • Received Date: 2021-10-28
  • Publish Date: 2022-04-25
    Abstract
  • Considering the strong visual interference such as the non-uniform lighting, water seepage, and noise in tunnel lining surfaces, a crack inspection method based on image blocks was designed for tunnel lining. A rapid and automatic non-contact intelligent inspection system for the tunnel structure appearance was developed according to the geographical characteristics in West China and the appearance diseases of tunnel lining. With the tunnel image data set under the non-uniform lighting as the research object, an image recognition algorithm for the characteristic extraction of tunnel cracks was proposed on the basis of image blocks. The noise generated by the electronic components was studied, and the hazard characteristics of tunnel lining were analyzed and summarized. The image matrix was divided into an appropriate number of area blocks in view of crack characteristics and resolution, and the original image was divided into the target-background area, target-disease area, disease-background area, and other areas according to the grayscale characteristics of these area blocks. Then, the rough image of tunnel cracks was obtained by the maximum inter-class variance method and the local threshold segmentation. On this basis, the crack characteristics of the rough image were extracted. Each area block of the original image was subjected to a contrast limited adaptive histogram equalization and a local threshold segmentation for a detailed image. The overlapping area of the detailed image and the rough image was set as the ideal image of crack binarization. On the basis of the inspection system for the tunnel structure appearance, binarization tests were carried out on the crack images in different directions, and the location information and directions of cracks in tunnel lining images were obtained by the positioning of tunnel cracks and the projection method. Research results reveal that the accuracy, recall, and F value of tunnel cracks under the proposed algorithm can reach 90.34%, 98.78%, and 94.37%, respectively. The proposed algorithm can not only ensure the integrity of tunnel cracks, but also retain the details of target cracks to the maximum extent under the non-uniform lighting. Thus, it can be used to deal with the binarization problem of general grayscale images. 1 tab, 13 figs, 37 refs.

     

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