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Open AccessArticle

Determination of Internal Elevation Fluctuation from CCTV Footage of Sanitary Sewers Using Deep Learning

1
Department of Land, Water, and Environment Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Korea
2
Department of Engineering Technology, Indiana University-Purdue University Indianapolis (IUPUI), Indianapolis, IN 46202, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Joong Hoon Kim
Water 2021, 13(4), 503; https://doi.org/10.3390/w13040503
Received: 10 January 2021 / Revised: 6 February 2021 / Accepted: 9 February 2021 / Published: 15 February 2021
(This article belongs to the Special Issue Machine Learning for Hydro-Systems)
The slope of sewer pipes is a major factor for transporting sewage at designed flow rates. However, the gradient inside the sewer pipe changes locally for various reasons after construction. This causes flow disturbances requiring investigation and appropriate maintenance. This study extracted the internal elevation fluctuation from closed-circuit television investigation footage, which is required for sanitary sewers. The principle that a change in water level in sewer pipes indirectly indicates a change in elevation was applied. The sewage area was detected using a convolutional neural network, a type of deep learning technique, and the water level was calculated using the geometric principles of circles and proportions. The training accuracy was 98%, and the water level accuracy compared to random sampling was 90.4%. Lateral connections, joints, and outliers were removed, and a smoothing method was applied to reduce data fluctuations. Because the target sewer pipes are 2.5 m concrete reinforced pipes, the joint elevation was determined every 2.5 m so that the internal slope of the sewer pipe would consist of 2.5 m linear slopes. The investigative method proposed in this study is effective with high economic feasibility and sufficient accuracy compared to the existing sensor-based methods of internal gradient investigation. View Full-Text
Keywords: convolutional neural network; water level; slope; image processing; semantic segmentation convolutional neural network; water level; slope; image processing; semantic segmentation
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MDPI and ACS Style

Ji, H.W.; Yoo, S.S.; Koo, D.D.; Kang, J.-H. Determination of Internal Elevation Fluctuation from CCTV Footage of Sanitary Sewers Using Deep Learning. Water 2021, 13, 503. https://doi.org/10.3390/w13040503

AMA Style

Ji HW, Yoo SS, Koo DD, Kang J-H. Determination of Internal Elevation Fluctuation from CCTV Footage of Sanitary Sewers Using Deep Learning. Water. 2021; 13(4):503. https://doi.org/10.3390/w13040503

Chicago/Turabian Style

Ji, Hyon W.; Yoo, Sung S.; Koo, Dan D.; Kang, Jeong-Hee. 2021. "Determination of Internal Elevation Fluctuation from CCTV Footage of Sanitary Sewers Using Deep Learning" Water 13, no. 4: 503. https://doi.org/10.3390/w13040503

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