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Copyright © 2021 Quanlei Wang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0/

Abstract

China is gradually transitioning from the “tunnel construction era” to the “tunnel maintenance era,” and more and more operating tunnels need to be inspected for diseases. With the continuous development of computer vision, the automatic identification of tunnel lining cracks with computers has gradually been applied in engineering. On the basis of summarizing the weaknesses and strengths of previous studies, this paper first uses the improved multiscale Retinex algorithm to filter the collected tunnel crack images and introduces the eight-direction Sobel edge detection operator to extract the edges of the cracks. Perform mathematical morphological operations on the image after edge extraction, and use the principle of the smallest enclosing rectangle to remove the isolated points of the image. Finally, the performance of the algorithm is judged by the objective evaluation index of the image, the accuracy of crack recognition, and the running time of the algorithm. The image filtering algorithm proposed in this paper can better preserve the edges of the image while enhancing the image. The objective evaluation indexes of the image have been improved significantly, and the main body of the crack can be accurately identified. The overall crack recognition accuracy rate can reach 97.5%, which is higher than the existing tunnel lining crack recognition algorithm, and the average calculation time for each image is shorter. This algorithm has high research significance and engineering application value.

Details

Title
Tunnel Lining Crack Recognition Based on Improved Multiscale Retinex and Sobel Edge Detection
Author
Wang, Quanlei 1   VIAFID ORCID Logo  ; Zhang, Ning 2 ; Jiang, Kun 2 ; Ma, Chao 2   VIAFID ORCID Logo  ; Zhou, Zhaochen 2 ; Dai, Chunquan 1   VIAFID ORCID Logo 

 School of Civil Engineering and Architecture, Shandong University of Science and Technology, Qingdao 266590, China; Shandong Civil Engineering Disaster Prevention and Mitigation Laboratory, Shandong University of Science and Technology, Qingdao 266590, China 
 School of Civil Engineering and Architecture, Shandong University of Science and Technology, Qingdao 266590, China 
Editor
Nicholas Fantuzzi
Publication year
2021
Publication date
2021
Publisher
John Wiley & Sons, Inc.
ISSN
1024123X
e-ISSN
15635147
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2580585539
Copyright
Copyright © 2021 Quanlei Wang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0/