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http://dx.doi.org/10.5762/KAIS.2017.18.2.590

Image Processing Algorithm for Crack Detection of Sewer with low resolution  

Son, Byung Jik (Department of International Civil & Plant Engineering, Konyang University)
Jeon, Joon Ryong (Department of International Civil & Plant Engineering, Konyang University)
Heo, Gwang Hee (Department of International Civil & Plant Engineering, Konyang University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.18, no.2, 2017 , pp. 590-599 More about this Journal
Abstract
In South Korea, sewage pipeline exploration devices have been developed using high resolution digital cameras of 2 mega-pixels or more. On the other hand, most devices are less than 300 kilo-pixels. Moreover, because 100 kilo-pixels devices are used widely, the environment for image processing is very poor. In this study, very low resolution ($240{\times}320$ = 76,800 pixels) images were adapted when it is difficult to detect cracks. Considering that the images of sewers in South Korea have very low resolution, this study selected low resolution images to be investigated. An automatic crack detection technique was studied using digital image processing technology for low resolution images of sewage pipelines. The authors developed a program to automatically detect cracks as 6 steps based on the MATLAB functions. In this study, the second step covers an algorithm developed to find the optimal threshold value, and the fifth step deals with an algorithm to determine cracks. In step 2, Otsu's threshold for images with a white caption was higher than that for an image without caption. Therefore, the optimal threshold was found by decreasing the Otsu threshold by 0.01 from the beginning. Step 5 presents an algorithm that detects cracks by judging that the length is 10 mm (40 pixels) or more and the width is 1 mm (4 pixels) or more. As a result, the crack detection performance was good despite the very low-resolution images.
Keywords
image processing; low resolution; sewer; CCTV; crack detection; user algorithm;
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