A new fire detection method using a multi-expert system based on color dispersion, similarity and centroid motion in indoor environment

Wang, Teng; Bu, Leping; Zhikai, Yang; Yuan, Peng; Ouyang, Jianquan

doi:10.1109/jas.2019.1911546

Cited by 25 publications

(6 citation statements)

References 16 publications

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“…Xi Tingyu et al [6] developed a rapid flame recognition algorithm based on a logarithmic regression model. Wang et al [7] proposed a novel fire detection method based on a multi-expert system. Xie et al [8] employed dynamic motion flicker features and deep static features for fire detection.…”

Section: Introductionmentioning

confidence: 99%

Flame detection algorithm based on adaptive Gaussian mixture model and DNCNN

Shi,

Wang,

Hao

2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

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In order to address the challenges of high false alarm rate, slow modeling speed, and poor real-time performance in flame detection algorithms under complex scenes, a lightweight and efficient two-stage video flame detection algorithm was designed.In the first stage, an Adaptive Gaussian Mixture Model (AGMM) is utilized to rapidly build the background model of the video image sequence and extract suspicious candidate regions from the sequence.In the second stage, a Deep Normalization and Convolutional Neural Network (DNCNN) is employed to discriminate the suspicious candidate regions, achieving accurate localization of flames.Compared to traditional classification algorithms, the proposed two-stage video flame detection algorithm effectively overcomes environmental disturbances in complex scenes, accurately and rapidly identifies flames, and demonstrates higher detection rates and adaptability.

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Section: Introductionmentioning

confidence: 99%

Flame detection algorithm based on adaptive Gaussian mixture model and DNCNN

Shi,

Wang,

Hao

2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

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“…Moreover, as time goes on, the centroid movement of flame can also be used as a basis for strengthening judgment. 5 However, in reality, the fire scene is complex and diverse with many uncertain factors. So only rely on artificial selection of one or two features for identification or detection, generalization is not strong.…”

Section: Introductionmentioning

confidence: 99%

“…It can be roughly divided into two stages, the traditional vision-based methods rely on artificial extraction feature, such as extraction of flame color, 3 texture, 4 and other features. Moreover, as time goes on, the centroid movement of flame can also be used as a basis for strengthening judgment 5 . However, in reality, the fire scene is complex and diverse with many uncertain factors.…”

Section: Introductionmentioning

confidence: 99%

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Lightweight anti-interference flame detection method based on context information

Xue

Wang

et al. 2022

J. Electron. Imag.

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As an important part of fire prevention and control, there are high standards for realtime, anti-interference, and accuracy about flame detection. At present, flame target detection methods lack comprehensive research on the above three indicators. To solve this problem, we propose a lightweight anti-interference flame detection method based on improved YOLOv4tiny. On the ground of dynamic characteristics of flame changing with time, a double-stream structure of flame detection model is designed. First, deeply separable convolution is applied to YOLOv4-tiny, resulting in a lighter backbone network. Second, in the feature extraction stage, the learning ability of the network to shallow features is improved by further integrating multiscale features, and the efficient channel attention module is introduced into the feature pyramid network to further improve the accuracy. Finally, intersection over union (IOU) postprocessing algorithm is used to shield the interference of fire-like targets effectively. Experimental results show that the parameter quantity of our methods are 3.95 and 4.22 MB, respectively, and accordingly their precisions reach 94.10% and 94.27%. Detection times are 35 and 46 ms, separately. In addition, after using IOU postprocessing algorithm, mAP and F1 composite index both increased to varying degrees, the consumption time is only 0.23 ms.

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“…The R-CNN was further supported by local spatial and temporal information like frame difference, color, similarity, wavelet transform, coefficient of variation, and mean square error (MSE), so that the value of false positive rate is significantly decreased. In [14], multi expert system was used for fire detection goal. The multi expert system was based on different contributors including color dispersion of region of interest, and similarity between consecutive frames and centroid motion.…”

Section: Introductionmentioning

confidence: 99%

A Privacy-Preserving Iot-Based Fire Detector

et al. 2021

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Fire detection has been an issue of interest to researchers due to its significant damage to lives and property within a very short time. One of the recent solutions developed to detect fire is to use Internetof-Things (IoT) devices equipped with cameras for surveillance. The captured videos of surroundings may be processed by the IoT devices themselves or at the cloud. The latter case is required if the detection algorithm is computationally demanding. However, the use of cloud has a flaw. In fact, using the cloud could pose the threat of having the privacy of a place violated, either through hacking or unauthorized access to the footage of the place where the cloud is installed. In this paper, a fire detection system that preserves the privacy of surroundings, while maintaining a high level of accuracy for fire detection is proposed. The proposed system makes use of the cloud for fire detection; and that is achieved by sending to the cloud features extracted from the video captured by the IoT device, instead of sending the actual footage. Binary video descriptors and Convolutional Neural Network (CNN) have been used to develop the fire detection algorithm. The video descriptors are used to extract features, while the CNN is used for classification. Videos with real fire and non-fire scenes have been used in this development. Results show that the performance of proposed fire detection algorithm can achieve 97.5% classification accuracy, that outperforms the state-of-the art algorithms which make direct use of raw videos. Therefore, the proposed fire detector is as reliable as other available systems, with the advantage of having a privacy-preserving capability. It is also demonstrated that the proposed video descriptors can be implemented for real-time processing using an IoT device, Raspberry Pi 4 platform, with an average processing speed of 100ms per frame, which well satisfies practical needs.

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A new fire detection method using a multi-expert system based on color dispersion, similarity and centroid motion in indoor environment

Cited by 25 publications

References 16 publications

Flame detection algorithm based on adaptive Gaussian mixture model and DNCNN

Flame detection algorithm based on adaptive Gaussian mixture model and DNCNN

Lightweight anti-interference flame detection method based on context information

A Privacy-Preserving Iot-Based Fire Detector

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