• Title/Summary/Keyword: Fire feature extraction

Search Result 9, Processing Time 0.028 seconds

Effects of Preprocessing and Feature Extraction on CNN-based Fire Detection Performance (전처리와 특징 추출이 CNN기반 화재 탐지 성능에 미치는 효과)

  • Lee, JeongHwan;Kim, Byeong Man;Shin, Yoon Sik
    • Journal of Korea Society of Industrial Information Systems
    • /
    • v.23 no.4
    • /
    • pp.41-53
    • /
    • 2018
  • Recently, the development of machine learning technology has led to the application of deep learning technology to existing image based application systems. In this context, some researches have been made to apply CNN (Convolutional Neural Network) to the field of fire detection. To verify the effects of existing preprocessing and feature extraction methods on fire detection when combined with CNN, in this paper, the recognition performance and learning time are evaluated by changing the VGG19 CNN structure while gradually increasing the convolution layer. In general, the accuracy is better when the image is not preprocessed. Also it's shown that the preprocessing method and the feature extraction method have many benefits in terms of learning speed.

Design Space Exploration of Embedded Many-Core Processors for Real-Time Fire Feature Extraction (실시간 화재 특징 추출을 위한 임베디드 매니코어 프로세서의 디자인 공간 탐색)

  • Suh, Jun-Sang;Kang, Myeongsu;Kim, Cheol-Hong;Kim, Jong-Myon
    • Journal of the Korea Society of Computer and Information
    • /
    • v.18 no.10
    • /
    • pp.1-12
    • /
    • 2013
  • This paper explores design space of many-core processors for a fire feature extraction algorithm. This paper evaluates the impact of varying the number of cores and memory sizes for the many-core processor and identifies an optimal many-core processor in terms of performance, energy efficiency, and area efficiency. In this study, we utilized 90 samples with dimensions of $256{\times}256$ (60 samples containing fire and 30 samples containing non-fire) for experiments. Experimental results using six different many-core architectures (PEs=16, 64, 256, 1,024, 4,096, and 16,384) and the feature extraction algorithm of fire indicate that the highest area efficiency and energy efficiency are achieved at PEs=1,024 and 4,096, respectively, for all fire/non-fire containing movies. In addition, all the six many-core processors satisfy the real-time requirement of 30 frames-per-second (30 fps) for the algorithm.

An Intelligent Automatic Early Detection System of Forest Fire Smoke Signatures using Gaussian Mixture Model

  • Yoon, Seok-Hwan;Min, Joonyoung
    • Journal of Information Processing Systems
    • /
    • v.9 no.4
    • /
    • pp.621-632
    • /
    • 2013
  • 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.

Smoke detection in video sequences based on dynamic texture using volume local binary patterns

  • Lin, Gaohua;Zhang, Yongming;Zhang, Qixing;Jia, Yang;Xu, Gao;Wang, Jinjun
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.11 no.11
    • /
    • pp.5522-5536
    • /
    • 2017
  • In this paper, a video based smoke detection method using dynamic texture feature extraction with volume local binary patterns is studied. Block based method was used to distinguish smoke frames in high definition videos obtained by experiments firstly. Then we propose a method that directly extracts dynamic texture features based on irregular motion regions to reduce adverse impacts of block size and motion area ratio threshold. Several general volume local binary patterns were used to extract dynamic texture, including LBPTOP, VLBP, CLBPTOP and CVLBP, to study the effect of the number of sample points, frame interval and modes of the operator on smoke detection. Support vector machine was used as the classifier for dynamic texture features. The results show that dynamic texture is a reliable clue for video based smoke detection. It is generally conducive to reducing the false alarm rate by increasing the dimension of the feature vector. However, it does not always contribute to the improvement of the detection rate. Additionally, it is found that the feature computing time is not directly related to the vector dimension in our experiments, which is important for the realization of real-time detection.

A Smoke Detection Method based on Video for Early Fire-Alarming System (조기 화재 경보 시스템을 위한 비디오 기반 연기 감지 방법)

  • Truong, Tung X.;Kim, Jong-Myon
    • The KIPS Transactions:PartB
    • /
    • v.18B no.4
    • /
    • pp.213-220
    • /
    • 2011
  • This paper proposes an effective, four-stage smoke detection method based on video that provides emergency response in the event of unexpected hazards in early fire-alarming systems. In the first phase, an approximate median method is used to segment moving regions in the present frame of video. In the second phase, a color segmentation of smoke is performed to select candidate smoke regions from these moving regions. In the third phase, a feature extraction algorithm is used to extract five feature parameters of smoke by analyzing characteristics of the candidate smoke regions such as area randomness and motion of smoke. In the fourth phase, extracted five parameters of smoke are used as an input for a K-nearest neighbor (KNN) algorithm to identify whether the candidate smoke regions are smoke or non-smoke. Experimental results indicate that the proposed four-stage smoke detection method outperforms other algorithms in terms of smoke detection, providing a low false alarm rate and high reliability in open and large spaces.

Fire Detection Approach using Robust Moving-Region Detection and Effective Texture Features of Fire (강인한 움직임 영역 검출과 화재의 효과적인 텍스처 특징을 이용한 화재 감지 방법)

  • Nguyen, Truc Kim Thi;Kang, Myeongsu;Kim, Cheol-Hong;Kim, Jong-Myon
    • Journal of the Korea Society of Computer and Information
    • /
    • v.18 no.6
    • /
    • pp.21-28
    • /
    • 2013
  • This paper proposes an effective fire detection approach that includes the following multiple heterogeneous algorithms: moving region detection using grey level histograms, color segmentation using fuzzy c-means clustering (FCM), feature extraction using a grey level co-occurrence matrix (GLCM), and fire classification using support vector machine (SVM). The proposed approach determines the optimal threshold values based on grey level histograms in order to detect moving regions, and then performs color segmentation in the CIE LAB color space by applying the FCM. These steps help to specify candidate regions of fire. We then extract features of fire using the GLCM and these features are used as inputs of SVM to classify fire or non-fire. We evaluate the proposed approach by comparing it with two state-of-the-art fire detection algorithms in terms of the fire detection rate (or percentages of true positive, PTP) and the false fire detection rate (or percentages of true negative, PTN). Experimental results indicated that the proposed approach outperformed conventional fire detection algorithms by yielding 97.94% for PTP and 4.63% for PTN, respectively.

Feature Extraction of Forest Fire by Using High Resolution Image (고해상도 위성영상을 이용한 산화피해림의 특징추출)

  • Yoon Bo-Yeol;Kim Choen
    • Proceedings of the KSRS Conference
    • /
    • 2006.03a
    • /
    • pp.275-278
    • /
    • 2006
  • 본 연구는 전정색(panchromatic) 고해상도 위성영상을 이용하여 산화피해림과 비산화림을 대상으로 수종별로 구분하여 조사하였다. 제안된 방법은 회색단계 공발생 행렬(Gray Level Co-occurrence Matrix, GLCM)을 통하여 생성된 질감 영상(textural images)과 웨이블릿 분해 영상(wavelet decomposition images)의 융합을 실시하여 질감 영상에서 추출될 수 있는 정보와 웨이블릿 분해를 통해 얻을 수 있는 정보를 획득하고자 하였다. 그 결과로 동일 수종을 형성하는 임반이나 산화피해 정도가 유사한 산림의 경우 영상의 밝기값의 분포가 일정한 범위 내에서 형성되어 수종 분류 및 산화피해 등급의 구분이 가능했으나, 영상 내 경계효과(edge effect) 현상은 일부 영상에서 나타났다.

  • PDF

The Threat List Acquisition Method in an Engagement Area using the Support Vector Machines (SVM을 이용한 교전영역 내 위협목록 획득방법)

  • Koh, Hyeseung
    • Journal of the Korea Institute of Military Science and Technology
    • /
    • v.19 no.2
    • /
    • pp.236-243
    • /
    • 2016
  • This paper presents a threat list acquisition method in an engagement area using the support vector machines (SVM). The proposed method consists of track creation, track estimation, track feature extraction, and threat list classification. To classify the threat track robustly, dynamic track estimation and pattern recognition algorithms are used. Dynamic tracks are estimated accurately by approximating a track movement using position, velocity and time. After track estimation, track features are extracted from the track information, and used to classify threat list. Experimental results showed that the threat list acquisition method in the engagement area achieved about 95 % accuracy rate for whole test tracks when using the SVM classifier. In case of improving the real-time process through further studies, it can be expected to apply the fire control systems.

Early Disaster Damage Assessment using Remotely Sensing Imagery: Damage Detection, Mapping and Estimation (위성영상을 활용한 실시간 재난정보 처리 기법: 재난 탐지, 매핑, 및 관리)

  • Jung, Myung-Hee
    • Journal of the Institute of Electronics Engineers of Korea CI
    • /
    • v.49 no.2
    • /
    • pp.90-95
    • /
    • 2012
  • Remotely sensed data provide valuable information on land monitoring due to multi-temporal observation over large areas. Especially, high resolution imagery with 0.6~1.0 m spatial resolutions contain a wealth of information and therefore are very useful for thematic mapping and monitoring change in urban areas. Recently, remote sensing technology has been successfully utilized for natural disaster monitoring such as forest fire, earthquake, and floods. In this paper, an efficient change detection method based on texture differences observed from high resolution multi-temporal data sets is proposed for mapping disaster damage and extracting damage information. It is composed of two parts: feature extraction and detection process. Timely and accurate information on disaster damage can provide an effective decision making and response related to damage.