Fire and smoke detection using wavelet analysis and disorder characteristics

Rafiee, Ali; Dianat, Reza; Jamshidi, Mehregan; Tavakoli, Reza; Abbaspour, Sara

doi:10.1109/iccrd.2011.5764295

Cited by 73 publications

(28 citation statements)

References 4 publications

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“…With this characterisation, it has been possible to determine which kind of images and algorithms have the highest efficiency. Also a new probabilistic fire segmentation algorithm is introduced and compared, to the other techniques, a high performance for flame detection processing can be obtained (Rafiee et al, 2011;Patil et al, 2015). Source: Kim and Wang (2009) 6 Motion and edge detection based flame detection…”

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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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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“…The differential threshold of moving object detection based on background difference could influence the effect of target detection. Because of smoke reunion characteristic [10], the block based differential threshold updating can guarantee within a certain time interval that the smoke can be accumulated with enough differences and formed potentially suspicious target formation. If the current block is detected as a suspicious object, it shows that this block differential threshold is proper.…”

Section: Smoke Image Segmentation Modelmentioning

confidence: 99%

Early Fire Smoke Image Segmentation in a Complex Large Space

Hu¹,

Wang²,

Zhao³

et al. 2015

TOBCTJ

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Aiming at the problem that the fire smoke image segmentation algorithm cannot obtain integral smoke information, the adaptive background updating smoke foreground object extraction algorithm based on block segmentation is proposed. According to the characteristics that the smoke internal pixels continuously roll in the heat of the driver and the pixel concentration from near the fire source to edge decreases in turn, the method for different blocks using different difference threshold is adopted to ensure the integrity of the extracted foreground target. Under the difference between the current frame and the background frame, block difference thresholds are updated to let the differential threshold adjustment with continuous adaptive monitoring. In the new weighted average updated background image corrected by the original background image, the interference suspicious target edge pixels are removed. Simulation experimental results show that the method is able to extract more complete information of smoke in a suspected area of the edge, and eliminate the interference of light and pedestrian in complex space.

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“…Next, absolute difference of RGB is compare with Th to find gray smoke color with following equation: In the proposed system dynamic characteristics of smoke such as disorder, direction is used for more accurate detection of smoke [10,11]. Since in the previous steps we separated the smoke color object but there might be things that confused with the smoke such as people, car, and shadows moving on, etc.…”

Section: Step2 Smoke Color Detectionmentioning

confidence: 99%

Smoke Detection in Video for Early Warning Using Static and Dynamic Features

Narwade¹

2013

IJRET

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An image processing approach for detection of smoke in video using static and dynamic features is proposed in this paper. As the conventional smoke detection such as particle sampling, smoke, temperature, Humidity Sensors needs to be close to the source of the smoke for detection, also they have weakness in wide coverage area and low response time. In order to overcome these shortcomings, this paper presents a method based on image processing techniques, capable to identify smoke from video taken from video dataset. The proposed detection method consists of the following steps: moving region detection, smoke color detection, detecting smoke disorder, direction and then classification phase. This will provide early warnings such as fire, thus reducing economic losses and casualties. In addition, it will help to improve the rate of smoke detection, as well as reducing the false detection rate of other suspected object. The paper is structured as follows: section 1 describes introduction part and features of smoke used to differentiate it from other suspected object. Then section 2 describes the previous work including conventional and recent research on smoke detection. Next, section 3 describes the proposed smoke detection system. Finally conclusion is given.

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Fire and smoke detection using wavelet analysis and disorder characteristics

Cited by 73 publications

References 4 publications

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

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

Early Fire Smoke Image Segmentation in a Complex Large Space

Smoke Detection in Video for Early Warning Using Static and Dynamic Features

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