Crack Detection of Underground Drainage Pipeline Based on Improved Linear Transformation Algorithm
Authors: 
Lu Qirong, Wu Zhijing, Lu ZirenAuthors Info & Claims
Lu Qirong, Wu Zhijing, Lu ZirenAuthors Info & ClaimsPages 549 - 554
Abstract
The Aiming at the problems of large noise interference in the detection of crack defects in underground sewage pipelines, and similar background and crack characteristics, an improved grayscale image linear transformation algorithm based on the preprocessing of two-dimensional gamma brightness equalization algorithm is proposed. First, use the two-dimensional gamma function brightness equalization algorithm to preprocess the image of the underground sewage pipe, and then use the improved linear transformation of the gray image to filter the noise. For Otsu threshold image segmentation, a new method is proposed that combines the results obtained by the traditional method and the improved method in this paper. The experimental results show that compared with the traditional linear transformation algorithm, this improved algorithm can retain more crack edge features, better filter image noise information, and have better visual effects. The final Otsu threshold segmentation combined method can also retain the characteristics of crack defects to the greatest extent.
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Index Terms
- Crack Detection of Underground Drainage Pipeline Based on Improved Linear Transformation Algorithm
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Published In
December 2020
718 pages
ISBN:9781450389099
DOI:10.1145/3453187
Copyright © 2020 ACM.
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- Guilin: Guilin University of Technology, Guilin, China
- International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong
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Association for Computing Machinery
New York, NY, United States
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Published: 24 March 2021
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EBIMCS 2020
EBIMCS 2020: 2020 3rd International Conference on E-Business, Information Management and Computer Science
December 5 - 6, 2020
Wuhan, China
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EBIMCS '20 Paper Acceptance Rate 112 of 566 submissions, 20%;
Overall Acceptance Rate 143 of 708 submissions, 20%
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