A Saliency-Based Method for Early Smoke Detection in Video Sequences

Jia, Yang; Yuan, Jie; Wang, Jinjun; Fang, Jun; Zhang, Xiaodan; Zhang, Yongming

doi:10.1007/s10694-014-0453-y

Cited by 65 publications

(30 citation statements)

References 28 publications

(40 reference statements)

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“…A motion saliency map of each frame is obtained with the method in [23], [29], [30]. Candidate pixels with small motions are discarded based on the selected threshold, while others are combined with the results of the color model in Section II.…”

Section: Hybrid Flame Detection System Based On Fusion Of Differmentioning

confidence: 99%

Autonomous Flame Detection in Videos With a Dirichlet Process Gaussian Mixture Color Model

Mihaylova

Isupova

et al. 2018

IEEE Trans. Ind. Inf.

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Abstract-This paper proposes a flame detection framework based on the color, dynamics and flickering properties of flames. The distribution of flame colors is modelled by a Gaussian Mixture Model whose number of Gaussian component is estimated by a Dirichlet process from training data rather than set empirically. The proposed approach estimates the flame color distribution more accurately as it can determine the number of Gaussian components of the mixture model automatically. Additionally, a probabilistic saliency analysis method and a one-dimensional wavelet transform are used to extract motion saliency and filtered temporal series as features, describing the dynamics and flickering properties of flames. The developed Dirichlet Process Gaussian Mixture Model based approach for autonomous flame detection is tested on various videos and achieves frame-wise accuracy higher than 95%.

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Section: Hybrid Flame Detection System Based On Fusion Of Differmentioning

confidence: 99%

Autonomous Flame Detection in Videos With a Dirichlet Process Gaussian Mixture Color Model

Mihaylova

Isupova

et al. 2018

IEEE Trans. Ind. Inf.

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“…Tung et al [14] employed a median method combined with a fuzzy c-means method to segment moving regions and cluster candidate smoke regions from moving regions. Jia et al [15] proposed a method of segmenting smoke regions based on saliency detection. Yuan [16] used a histogram sequence of local binary pattern (LBP) and local binary pattern variance (LBPV) pyramids to detect smoke.…”

Section: Related Workmentioning

confidence: 99%

Detection of Wildfire Smoke Images Based on a Densely Dilated Convolutional Network

Zhao

Zhang

et al. 2019

Electronics

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Recently, many researchers have attempted to use convolutional neural networks (CNNs) for wildfire smoke detection. However, the application of CNNs in wildfire smoke detection still faces several issues, e.g., the high false-alarm rate of detection and the imbalance of training data. To address these issues, we propose a novel framework integrating conventional methods into CNN for wildfire smoke detection, which consisted of a candidate smoke region segmentation strategy and an advanced network architecture, namely wildfire smoke dilated DenseNet (WSDD-Net). Candidate smoke region segmentation removed the complex backgrounds of the wildfire smoke images. The proposed WSDD-Net achieved multi-scale feature extraction by combining dilated convolutions with dense block. In order to solve the problem of the dataset imbalance, an improved cross entropy loss function, namely balanced cross entropy (BCE), was used instead of the original cross entropy loss function in the training process. The proposed WSDD-Net was evaluated according to two smoke datasets, i.e., WS and Yuan, and achieved a high AR (99.20%) and a low FAR (0.24%). The experimental results demonstrated that the proposed framework had better detection capabilities under different negative sample interferences.

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“…In [27], salient convolutional neural networks based on pixel-level and object-level extracted smoke saliency map information were used. In [28], a saliency detection model was applied to segment a smoke region based on pixel colour and motion features. In this paper, an end-to-end framework for video smoke detection is proposed.…”

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 Saliency-Based Method for Early Smoke Detection in Video Sequences

Cited by 65 publications

References 28 publications

Autonomous Flame Detection in Videos With a Dirichlet Process Gaussian Mixture Color Model

Autonomous Flame Detection in Videos With a Dirichlet Process Gaussian Mixture Color Model

Detection of Wildfire Smoke Images Based on a Densely Dilated Convolutional Network

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

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