Determination of Internal Elevation Fluctuation from CCTV Footage of Sanitary Sewers Using Deep Learning
| Autor: | Jeong-Hee Kang, Hyon Wook Ji, Dan D. Koo, Sung Soo Yoo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
lcsh:Hydraulic engineering
Geography Planning and Development Flow (psychology) education 0211 other engineering and technologies 0207 environmental engineering Sewage convolutional neural network 02 engineering and technology Aquatic Science Biochemistry lcsh:Water supply for domestic and industrial purposes lcsh:TC1-978 021105 building & construction Sanitary sewer 020701 environmental engineering Joint (geology) Water Science and Technology lcsh:TD201-500 business.industry Elevation Training (meteorology) water level semantic segmentation Water level image processing slope Environmental science business Smoothing Marine engineering |
| Zdroj: | Water Volume 13 Issue 4 Water, Vol 13, Iss 503, p 503 (2021) |
| ISSN: | 2073-4441 |
| DOI: | 10.3390/w13040503 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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