An efficient hybrid algorithm for fire flame detection

Khatami, Amin; Mirghasemi, Saeed; Khosravi, Abbas; Nahavandi, Saeid

doi:10.1109/ijcnn.2015.7280590

Cited by 7 publications

(7 citation statements)

References 17 publications

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“…However, the false-positive rate is still high in this approach. Khatami et al [26], [27] are the most straightforward image processing algorithms for localized fire regions. The algorithm was created a fire color space using a weighting filter.…”

Section: Resultsmentioning

confidence: 99%

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Real-time Building Fire Detection and Segmentation in Video using Convolutional Neural Networks with Gaussian threshold approach

Sridhar

Thangavel

Parameswaran

et al. 2022

Preprint

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Computer vision-based fire flame detection system has recently begun to replace traditional fire detection systems. The conventional imaging-based system uses color models and temporal analysis algorithms to detect and segment fire in an image or a frame. Color model algorithms could not effectively distinguish between fire and fire-like objects in real-time videos. Besides, temporal analysis between structures has not yielded higher detection accuracies. This work proposes a lightweight convolutional neural network (CNN) combined with Gaussian distribution threshold probability to detect and segment the fire in a frame sequence. The proposed hybrid work extracted the features and automatically classified the frames using simple network architecture, and the Gaussian probability threshold method improves the detection of fire region localization. Experimental performance of the proposed work increases the detection accuracy and better false alarm performance. This paper can predict the severity of the fire, area, and centroid, fire probability. The proposed work using low-resolution frames will surely help the real-time implementation system.

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Section: Resultsmentioning

confidence: 99%

“…This work yields a high false-negative alarm. Amin Khatami et al [26], [27] proposed an early and accurate automated fire detection algorithm based on a computer-vision approach. This work has addressed early fire detection by using the image processing technique.…”

Section: Related Workmentioning

confidence: 99%

Real-time Building Fire Detection and Segmentation in Video using Convolutional Neural Networks with Gaussian threshold approach

Sridhar

Thangavel

Parameswaran

et al. 2022

Preprint

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“…The fire-flame color is used as the first marker in a significant amount of published studies. Even if new color space was published by Khatami et al [3,4], many algorithms use simplified color rules. Their work [3,4] was aimed to develop a method for fire clustering by color.…”

Section: Color Detectionmentioning

confidence: 99%

“…Even if new color space was published by Khatami et al [3,4], many algorithms use simplified color rules. Their work [3,4] was aimed to develop a method for fire clustering by color. Swarm algorithms were used for defining coefficients of the convolutional matrix in order to select fire-like areas.…”

Section: Color Detectionmentioning

confidence: 99%

Fire Detection in Video Stream by Using Simple Artificial Neural Network

Janku

Oplatková

Dulík

et al. 2018

mendel

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This paper deals with the preliminary research of the fire detection in a video stream. Early fire detection can save lives and properties from huge losses and damages. Therefore the surveillance of the areas is necessary. Early fire discovery with high accuracy, i.e. a low number of false positive or false negative cases, is essential in any environment, especially in places with the high motion of people. The traditional fire detection sensors have some drawbacks: they need separate systems and infrastructure to be implemented, to use sensors in the case of the industrial environment with open fire technologies is often impossible, and others. The fire detection in a video stream is one of the possible and feasible solutions suitable for replacement or supplement of conventional fire detection sensors without a need for installation a huge infrastructure. The paper provides the state of the art in the fire detection. The following part of the paper proposes the new system of feature extraction and describes the feedforward neural network which was used for the training and testing of the proposed idea. The promising results are presented with over 93% accuracy on a selected dataset of movies which consist of more and highly varied instances than published by other researchers involved in the fire detection field. The structure of the neural networks promises higher computational speed than currently implemented deep learning systems.

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“…Consequently, researchers in [16][17][18] adopted different colour spaces, for example, HSV to better represent fire pixels. On the other hand, authors in [19][20][21][22] introduced new flame-based colour spaces to contrast between fire and non-fire pixels; which can be subsequently separated using Otsu thresholding algorithm [23]. A 3 × 3 conversion/transform matrix was used to convert pixels from RGB to the new colour space.…”

Section: Introductionmentioning

confidence: 99%

A new flame‐based colour space for efficient fire detection

Giwa,

Benkrid

2023

IET Image Processing

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Computer vision‐based fire detection algorithms have been extensively researched over the past decades as they offer reliable and efficient solutions to early fire detection, as well as economical solutions in outdoor environments. The proposed systems have different levels of complexity; nevertheless, they rely heavily on colour based fire segmentation as the first processing stage using different colour spaces. This paper presents a new flame‐based colour space which accentuates the difference between fire and non‐fire pixel values. The hybrid Artificial Bee Colony‐Teaching Learning‐based Optimisation (ABC‐TLBO) and the k‐medoids clustering algorithm along with a Feature image constructed manually from a wide dataset of fire and non‐fire pixels were used to deduce the optimised colour space conversion matrix. The latter can then be used to transform the RGB pixels from a camera feed into the new Flame‐based colour space for a more efficient fire segmentation. The proposed colour space is capable to separate fire and non‐fire pixels into two intensity classes, whereby the resulting between‐class variance is significantly widened; this maximises the effectiveness of the binary thresholding to separate fire from non‐fire pixels. The experimental results demonstrate that the proposed flame‐based colour space provides a superior average qualitative and quantitative performance on benchmark datasets compared to state‐of‐the‐art colour models; a 20% average increase in F1 score demonstrates a better balance between fire detection and false alarm rates. Moreover, the computation time of the fire detection system is faster by 15.6% in average. The proposed system provides a better implementation of the colour‐based fire segmentation processing stage in vision‐based fire detection systems; it vindicates, in this application context, the superiority of flame‐tuned colour space over conventional ones.

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An efficient hybrid algorithm for fire flame detection

Cited by 7 publications

References 17 publications

Real-time Building Fire Detection and Segmentation in Video using Convolutional Neural Networks with Gaussian threshold approach

Real-time Building Fire Detection and Segmentation in Video using Convolutional Neural Networks with Gaussian threshold approach

Fire Detection in Video Stream by Using Simple Artificial Neural Network

A new flame‐based colour space for efficient fire detection

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