Flame Detection using Image Processing Techniques

Patel, Punam; Tiwari, Shamik

doi:10.5120/9381-3817

Cited by 28 publications

(7 citation statements)

References 8 publications

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“…Regarding the use of color as a way of flame features detection, color models such as RGB [1], [2] HSV [3], YCbCr [3], [4], YUV [5], [6] or their combinations [7] are widely used.…”

Section: Related Workmentioning

confidence: 99%

Core Generator of Hypotheses for Real-Time Flame Detecting

Peleshko

Maksymiv

Rak

et al. 2018

2018 IEEE Second International Conference on Data Stream Mining &Amp; Processing (DSMP)

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The flame is a visually unstable and constantly changeable process, which causes considerable difficulties for its detection in the video streams. Although the modern architecture of convolutional neural networks can show high accuracy, their integration into real-time systems is problematic, because they require a large amount of computing resources. To reduce the number of these resources, it is proposed to select possible regions of interest (ROI), which are based on the developed generator of hypotheses. Compared to existing flame detection algorithms, the developed generator of hypotheses allows you to work with the minimum of computing resources and has a high degree of classification completeness due to improved methods of color segmentation and moving objects detection.

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“…Regarding the use of color as a way of flame features detection, color models such as RGB [1], [2] HSV [3], YCbCr [3], [4], YUV [5], [6] or their combinations [7] are widely used.…”

Section: Related Workmentioning

confidence: 99%

Core Generator of Hypotheses for Real-Time Flame Detecting

Peleshko

Maksymiv

Rak

et al. 2018

2018 IEEE Second International Conference on Data Stream Mining &Amp; Processing (DSMP)

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“…Thus, color spaces, such as YCbCr, YUV, and CIE Lab, can be promising candidates for the purpose of fire-like region detection, in which the chrominance components (Cb and Cr in the YCbcCr color model, U and V in the YUV color model, a and b in the CIE Lab color model) and luminance component (Y in both YCbCr and YUV color models, L in the CIE Lab color model) of a movie frame can be processed independently. According to [45][46][47], the YCbCr color space is more effective for distinguishing luminance information from chrominance information than other color spaces. Thus, the YCbCr color space is used for the multi-stage fire detection algorithm.…”

Section: Movement-containing Region Detection (Mcrd) Based On Backgromentioning

confidence: 99%

An optimal many-core model-based supercomputing for accelerating video-equipped fire detection

et al. 2015

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Automatic fire detection has become more and more appealing because of the increasing use of video capabilities in surveillance systems used for early detection of fire. However, its high computational complexities limit its use in real-time applications. To meet the real-time processing of today's fire detection techniques, this study proposes a single instruction, multiple data many-core model. To design an efficient many-core model for image processing applications such as fire detection, a key design parameter is the image data-per-processing-element (IDPE) variation of the many-core system, which is the amount of image data directly mapped to each processing element PE. This study quantitatively evaluates the impact of the IDPE variation on system performance and energy efficiency for the multi-stage fire detection approach that consists of movement-containing region detection, color segmentation, fire feature extraction of fires, and decision making if there is a fire or non-fire in a processing video frame. In this study, we use six IDPE ratios to determine an optimal many-core model that provides the most efficient operation for fire detection using architectural and workload simulation. Experimental results indicate that the most efficient many-core model is achieved at the 64 IDPE value in terms of the worst-case execution time and energy 123 J. Seo et al. efficiency. In addition, this study compares the performance of the most efficient manycore configuration with that of a commercial graphics processing unit (Nvidia GeForce GTX 480) to show the improved performance of the proposed many-core model for the fire detection algorithm. This many-core configuration outperforms the commercial graphic processing unit in the worst-case execution time and energy efficiency.

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“…This training reduces the false alarm rates. D-Markov machines are constructed to extract features of the dynamic characteristics in such a way that the image features analysis can be used to detect a fire more quickly (Horng and Peng, 2008;Patel and Tiwari, 2012). This approach is suitable for wildland fire using colour segmentation algorithms introduced for the analysis of HSI space.…”

Section: Hsi-based Smoke and Fire Detectionmentioning

confidence: 99%

State of the art of smoke and fire detection using image processing

Umar

Silva

Bakar

et al. 2017

IJSISE

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In this paper we present a comprehensive review of the state of the art of smoke and fire detection techniques using image processing. Smoke is a good indicator of a pre-fire condition and many fires are indicators of subsequent dangerous situations due to the spread of fire. In this paper we first start our comparison of smoke detection methods and different types of approaches for the classification of smoke. Furthermore we analyse different types of technologies and various models involve in detection techniques such as RGB and HSI models for detecting smoke and fire. Generally, the false alarm rate can be reduced by image processing through effective types of detection techniques such as vision-based or sensor-based methods. Mainly in this paper, we focus on optimised technologies in order to detect smoke and fire at the earliest possible stage of the event, and smoke and fire detection by satellite vision methods.

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Flame Detection using Image Processing Techniques

Cited by 28 publications

References 8 publications

Core Generator of Hypotheses for Real-Time Flame Detecting

Core Generator of Hypotheses for Real-Time Flame Detecting

An optimal many-core model-based supercomputing for accelerating video-equipped fire detection

State of the art of smoke and fire detection using image processing

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