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- 주제어 : Forest smoke detection (검색결과 7 건)
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( Seok Hwan Yoon ),( Joon Young Min ) 한국정보처리학회 2013 Journal of information processing systems Vol.9 No.4
The most important things for a forest fire detection system are the exact extraction of the smoke from image and being able to clearly distinguish the smoke from those with similar qualities, such as clouds and fog. This research presents an intelligent forest fire detection algorithm via image processing by using the Gaussian Mixture model (GMM), which can be applied to detect smoke at the earliest time possible in a forest. GMMs are usually addressed by making the model adaptive so that its parameters can track changing illuminations and by making the model more complex so that it can represent multimodal backgrounds more accurately for smoke plume segmentation in the forest. Also, in this paper, we suggest a way to classify the smoke plumes via a feature extraction using HSL(Hue, Saturation and Lightness or Luminanace) color space analysis.
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YCbCr 컬러 모델에서의 조건 검사와 적응적 차영상을 이용한 화염 및 연기 검출 알고리즘
이두희(Doo-Hee Lee),유재욱(Jae-Wook Yoo),이강희(Kang-Hee Lee),김윤(Yoon Kim) 한국컴퓨터정보학회 2010 韓國컴퓨터情報學會論文誌 Vol.15 No.5
본 논문에서는 감시 카메라를 통해 입력된 영상 정보로 연기와 화염을 실시간 검출하는 알고리즘을 제안한다. 산불은 막대한 인명, 재산피해를 불러오기 때문에 조기 감지에 따른 초기 진화가 매우 중요하다. 제안하는 산불 감시 알고리즘은 화염 감지와 연기 감지로 나뉘는데, 화염 감지는 단일 프레임에서 YCbCr 컬러 모델에서의 조건 검사를 통하여 화염을 검출한다. 연기 감지를 위해서는 먼저 현재 영상과 인접한 프레임들의 평균 영상사이의 차를 가중치로 이용하여 배경 범위를 설정하고, 이 범위를 벗어나면서 회색조를 갖는 픽셀만을 연기영역으로 검출한다. 제안하는 화염 감지 알고리즘은 기존의 알고리즘보다 일조량에 따른 조도의 변화에 강건하고, 연기 검출 알고리즘은 단위 시간동안의 변화량을 고려하여 회색조의 픽셀만을 연기로 감지하기 때문에 효과적인 조기 산불 탐지가 가능하다. 실험 결과는 제안하는 산불 감시 알고리즘이 기존의 알고리즘보다 우수한 성능을 나타냄을 보여준다. In this paper, we propose a new real-time algorithm detecting the flame and smoke in digital CCTV images. Because the forest fire causes the enormous human life and damage of property, the early management according to the early sensing is very important. The proposed algorithm for monitoring forest fire is classified into the flame sensing and detection of smoke. The flame sensing algorithm detects a flame through the conditional test at YCbCr color model from the single frame. For the detection of smoke, firstly the background range is set by using differences between current picture and the average picture among the adjacent frames in the weighted value, and the pixels which get out of this range and have a gray-scale are detected in the smoke area. Because the proposed flame sensing algorithm is stronger than the existing algorithms in the change of the illuminance according to the quantity of sunshine, and the smoke detection algorithm senses the pixel of a gray-scale with the smoke considering the amount of change for unit time, the effective early forest fire detection is possible. The experimental results indicate that the proposed algorithm provides better performance than existing algorithms.
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윤석환,민준영 한국정보처리학회 2013 Journal of information processing systems Vol.9 No.4
The most important things for a forest fire detection system are the exact extraction of the smoke from image and being able to clearly distinguish the smoke from those with similar qualities, such as clouds and fog. This research presents an intelligent forest fire detection algorithm via image processing by using the Gaussian Mixture model (GMM), which can be applied to detect smoke at the earliest time possible in a forest. GMMs are usually addressed by making the model adaptive so that its parameters can track changing illuminations and by making the model more complex so that it can represent multimodal backgrounds more accurately for smoke plume segmentation in the forest. Also, in this paper, we suggest a way to classify the smoke plumes via a feature extraction using HSL(Hue, Saturation and Lightness or Luminanace) color space analysis.
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Yoon, Seok-Hwan,Min, Joonyoung Korea Information Processing Society 2013 Journal of information processing systems Vol.9 No.4
The most important things for a forest fire detection system are the exact extraction of the smoke from image and being able to clearly distinguish the smoke from those with similar qualities, such as clouds and fog. This research presents an intelligent forest fire detection algorithm via image processing by using the Gaussian Mixture model (GMM), which can be applied to detect smoke at the earliest time possible in a forest. GMMs are usually addressed by making the model adaptive so that its parameters can track changing illuminations and by making the model more complex so that it can represent multimodal backgrounds more accurately for smoke plume segmentation in the forest. Also, in this paper, we suggest a way to classify the smoke plumes via a feature extraction using HSL(Hue, Saturation and Lightness or Luminanace) color space analysis.
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A video-based SlowFastMTB model for detection of small amounts of smoke from incipient forest fires
Choi Minseok,Kim Chungeon,Oh Hyunseok 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.2
This paper proposes a video-based SlowFast model that combines the SlowFast deep learning model with a new boundary box annotation algorithm. The new algorithm, namely the MTB (i.e., the ratio of the number of Moving object pixels To the number of Bounding box pixels) algorithm, is devised to automatically annotate the bounding box that includes the smoke with fuzzy boundaries. The model parameters of the MTB algorithm are examined by multifactor analysis of variance. To demonstrate the validity of the proposed approach, a case study is provided that examines real video clips of incipient forest fires with small amounts of smoke. The performance of the proposed approach is compared with those of existing deep learning models, including convolutional neural network (CNN), faster region-based CNN (faster R-CNN), and SlowFast. It is demonstrated that the proposed approach achieves enhanced detection accuracy, while reducing false negative rates.
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단일 및 다중 영상 평균을 이용한 화염 및 연기 검출 알고리즘
이두희,김윤 강원대학교 정보통신연구소 2010 정보통신논문지 Vol.14 No.-
본 논문에서는 CCTV를 통해 입력된 영상 정보로 연기와 화염을 실시간 검출하는 알고리즘을 제안한다. 산불 은 막대한 인명,재산피해를 불러오기 때문에 조기 감지에 따른 초기 진화가 매우 중요하다. 산불 감시 알고 리즘은 화염 감지와 연기감지로 나뉘는데 화염 감지는 단일 프레임에서 영상 성분 평균을 이용한 조건검사를 통하여 검출한다. 또한 연기 감지는 인접한 프레임간의 평균 영상과 현재 영상과의 차를 가중치로 이용하여 배경 범위를 설정하고 이 범위를 벗어난 영역 중에 회색조를 갖는 픽셀만 연기영역으로 검출한다. 위와 같은 화염 감지 알고리즘은 기존의 알고리즘보다 일조량에 따른 조도의 변화에 강건하며,연기 검출 알고리즘도 단위 시간동안의 변화량을 고려하고 회색조의 픽셀만을 연기로 감지하기 때문에 효과적인 조기 산불 탐지가 가능하다.
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