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Conference Paper
Deep Neural Networks for Wild fire Detection with Unmanned Aerial Vehicle
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- Authors
- Wonjae Lee, Seonghyun Kim, Yong-Tae Lee, Hyun-Woo Lee, Min Choi
- Issue Date
- 2017-01
- Citation
- International Conference on Consumer Electronics (ICCE) 2017, pp.271-272
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Wildfire threatens people's lives and livelihoods. Wildfires kill hundreds of thousand people worldwide each year. Disaster information services are required to save lives and reduce economic losses when wildfire occurs. However, manned airplanes are too expensive to operate for frequent wildland monitoring. Satellite images cannot be used for early wildfire detection due to low temporal resolution and low spatial resolution. Unmanned aerial vehicles are cost-effective means to provide high resolution images for wildfire detection. A wildfire detection system utilizing unmanned aerial vehicles was developed with deep convolutional neural networks. The system achieved high accuracy for wide range of aerial photographs.
- KSP Keywords
- Aerial photographs, Convolution neural network(CNN), Deep convolutional neural networks, Deep neural network(DNN), Disaster information, Economic loss, Fire detection, High accuracy, Information services, Intrusion detection system(IDS), Temporal resolution