A video-based smoke detection using smoke flow pattern and spatial-temporal energy analyses for alarm systems

Appana, Dileep Kumar; Islam, Rashedul; Khan, Sheraz Ali; Kim, Jong‐Myon

doi:10.1016/j.ins.2017.08.001

Cited by 53 publications

(12 citation statements)

References 7 publications

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“…5 Visual images for smoke detection cannot be achieved due to the dark cargo environment. Hence, infrared image frames considering the visual smoke detection 6 algorithms are used to obtain smoke features. Due to complexity and cost, there is no infrared camera for smoke detection in the cargo hold of aircraft in the world.…”

Section: Introductionmentioning

confidence: 99%

Detection of smoke from infrared image frames in the aircraft cargoes

Deng

Chen

et al. 2021

International Journal of Distributed Sensor Networks

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The existing equipment of civil aircraft cargo fire detection mainly uses photoelectric smoke detectors, which has a high false alarm rate. According to Federal Aviation Agency’s statistics, the false alarm rate is as high as 99%. Since, in the cargo of civil aircraft, visible image processing technology cannot be used to detect smoke in the event of a fire due to the closed dark environment, a novel smoke detection method using infrared image processing technology is presented. Experiments were conducted under different environment pressures in the full-size cargo of civil aircraft. The results show that the proposed method can effectively detect smoke at the early stage of fire which is applicable for fire detection in civil aircraft cargoes.

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Section: Introductionmentioning

confidence: 99%

Detection of smoke from infrared image frames in the aircraft cargoes

Deng

Chen

et al. 2021

International Journal of Distributed Sensor Networks

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“…The RGB color system was used to extract fi re pixels and smoke pixels, which are then verifi ed if they are real fi re pixels or fi re-like pixels. [3,4] attempted to detect fi res and fl ames by processing the video data generated by an ordinary camera monitoring a scene. The fi re pixels are determined by ordinary motion, RGB color clues, and video analysis in the wavelet domain.…”

Section: Introductionmentioning

confidence: 99%

Fire Detection Methods Based on Various Color Spaces and Gaussian Mixture Models

Munshi¹

2021

Adv. Sci. Technol. Res. J.

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Fire disasters are very serious problems that may cause damages to ecological systems, infrastructure, properties, and even a threat to human lives; therefore, detecting fi res at their earliest stage is of importance. Inspired by the technological advancements in artifi cial intelligence and image processing in solving problems in diff erent applications, this encourages adopting those technologies in reducing the damage and harm caused by fi re. This study attempts to propose an intelligent fi re detection method by investigating three approaches to detect fi re based on three diff erent color models: RGB, YCbCr, and HSV were presented. The RGB method is applied based on the relationship among the red, green and blue values of pixels in images. In the YCbCr color model, image processing and machine learning techniques are used for morphological processing and automatic recognition of fi re images. In turn, for HSV, supervised machine learning techniques are adopted, namely decision rule and Gaussian mixture model (GMM). Further, the expectation maximization (EM) algorithm was deployed for the GMM parameters estimation. The three proposed models were tested on two data sets, one of which contains fi re images, the other consists of non-fi re images with some having fi re-like colors to test the effi ciency of the proposed methods. The experimental results showed that the overall accuracies on two data sets for the RGB, YCbCr, and HSV methods were satisfactory and were effi cient in detecting the outdoor and indoor fi res.

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“…Smoke video detection and analysis tasks often have difficulty obtaining ideal performance because of the multiformity of form, swing, changing smoke colour tones, environmental illumination, and low-resolution images of forest scenes. Traditional video smoke detection methods based on pattern recognition [2] and digital image processing [3] techniques depend on obtaining ample dynamic texture [4], colour features [5] [6], optical flows [7] and spatial features [8] [9]. Gubbi et al [10] adopted a pattern recognition method that manually divides the smoke video frame into 32×32 pixels to detect smoke from datasets based on wavelets [11] and support vector machines [12].…”

Section: Introductionmentioning

confidence: 99%

Wildfire Smoke Detection Based on Depthwise Separable Convolutions and Target-Awareness

Zhao¹,

Zhang²,

Zhang³

et al. 2020

Preprint

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Since smoke usually occurs before a flame arises, fire smoke detection is especially significant for early warning systems. In this paper, a DSATA(Depthwise Separability And Target Awareness) algorithm based on depthwise separability and target awareness is proposed. Existing deep learning methods with convolutional neural networks pretrained by abundant and vast datasets are always used to realize generic object recognition tasks. In the area of smoke detection, collecting large quantities of smoke data is a challenging task for small sample smoke objects. The basis is that the objects of interest can be arbitrary object classes with arbitrary forms. Thus, deep feature maps acquired by target-aware pretrained networks are used in modelling these objects of arbitrary forms to distinguish them from unpredictable and complex environments. In this paper, this scheme is introduced to deal with smoke detection. The depthwise separable method with a fixed convolution kernel replacing the training iterations can improve the speed of the algorithm to meet the enhanced requirements of real-time fire spreading for detecting speed. The experimental results demonstrate that the proposed algorithm can detect early smoke, is superior to the state-of-the-art methods in accuracy and speed, and can also realize real-time smoke detection.

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A video-based smoke detection using smoke flow pattern and spatial-temporal energy analyses for alarm systems

Cited by 53 publications

References 7 publications

Detection of smoke from infrared image frames in the aircraft cargoes

Detection of smoke from infrared image frames in the aircraft cargoes

Fire Detection Methods Based on Various Color Spaces and Gaussian Mixture Models

Wildfire Smoke Detection Based on Depthwise Separable Convolutions and Target-Awareness

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