Water Level Detection from CCTV Cameras using a Deep Learning Approach
| Autor: | Punyanuch Borwarnginn, Jason H. Haga, Worapan Kusakunniran |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Emergency management
Computer science business.industry Deep learning Real-time computing Training (meteorology) 02 engineering and technology 010501 environmental sciences 01 natural sciences River water Flooding (computer networking) Water level 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Flood detection Artificial intelligence Precipitation Natural disaster business 0105 earth and related environmental sciences |
| Zdroj: | TENCON |
| DOI: | 10.1109/tencon50793.2020.9293865 |
| Popis: | Natural disasters are a global problem that causes widespread losses and damage. A system to provide timely information is required in order to help reduce losses. Flooding is one of the major natural disasters that requires a monitoring and detection system. The traditional flood detection systems use remote sensors such as river water levels and rainfall to provide information to both disaster management professionals and the general public. There is an attempt to use visual information such as CCTV cameras to detect extreme flooding events; however, it requires human experts and consistent attention to monitor any changes. In this paper, we introduce an approach to the automatic river water level detection using deep learning to determine the water level from surveillance cameras. The model achieves 93% accuracy using a single camera location and 83% accuracy using multiple camera locations. |
| Databáze: | OpenAIRE |
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