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학술대회 Early Wildfire Detection Using Convolutional Neural Network
Cited 3 time in scopus Download 0 time Share share facebook twitter linkedin kakaostory
저자
오선호, 김상원, 정순기, 김건우
발행일
202002
출처
International Workshop on Frontiers of Computer Vision (IW-FCV) 2020 (CCIS 1212), pp.18-30
DOI
https://dx.doi.org/10.1007/978-981-15-4818-5_2
협약과제
19HH6100, 선제적 위험대응을 위한 예측적 영상보안 핵심기술 개발, 김건우
초록
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.
키워드
Deep learning, Early wildfire detection, Video surveillance
KSP 제안 키워드
Convolution neural network(CNN), Ecological systems, Large data sets, Material damage, Video surveillance system, deep learning(DL), flame detection, learning-based method, light-weight, loss of life