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Conference Paper
Early Wildfire Detection Using Convolutional Neural Network
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- Authors
- Seon Ho Oh, Sang Won Ghyme, Soon Ki Jung, Geon-Woo Kim
- Issue Date
- 2020-02
- Citation
- International Workshop on Frontiers of Computer Vision (IW-FCV) 2020 (CCIS 1212), pp.18-30
- Publisher
- Springer
- Language
- English
- Type
- Conference Paper
- Abstract
- Wildfires are one of the disasters that are difficult to detect early and cause significant damage to human life, ecological systems, and infrastructure. There have been several research attempts to detect wildfires based on convolutional neural networks (CNNs) in video surveillance systems. However, most of these methods only focus on flame detection, thus they are still not sufficient to prevent loss of life and reduce economic and material damage. To tackle this issue, we present a deep learning-based method for detecting wildfires at an early stage by identifying flames and smokes at once. To realize the proposed idea, a large dataset for wildfire is acquired from the web. A light-weight yet powerful architecture is adopted to balance efficiency and accuracy. And focal loss is utilized to deal with the imbalance issue between classes. Experimental results demonstrate the effectiveness of the proposed method and validate its suitability for early wildfire detection in a video surveillance system.
- KSP Keywords
- Convolution neural network(CNN), Ecological systems, Large datasets, Light-weight, Material damage, Video surveillance system, deep learning(DL), flame detection, learning-based method, loss of life, neural network(NN)