Recurrent convolutional network for video-based smoke detection

Yin, Mengxia; Lang, Congyan; Li, Zun; Feng, Songhe; Wang, Tao

doi:10.1007/s11042-017-5561-5

Cited by 50 publications

(34 citation statements)

References 26 publications

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“…However, there are many moving object in some scenes, and it is difficult to accurately identify the smoke objects due to motion features. In general, the AR and RR values of smoke detection methods based on deep features( such as references [14,[16][17][18][19] and our method ) are higher than that of traditional features (such as references [3,5,11]). That is because the deep features learned through big data can more fully mine the smoke features with stronger discrimination ability.…”

Section: Performance Comparison (1) Two-stage Smoke Detection Performmentioning

confidence: 83%

“…Combining the deep features with motion features helps further reduce false detections.Yin et al [17] proposed a RCN (Recurrent Convolutional Network) for video-based smoke detection. This method first captured the space and motion context information by using deep convolutional motion-space networks, and then trained the smoke model by using a temporal pooling layer and RNNs.…”

Section: Related Workmentioning

confidence: 99%

“…Table 6 compares the performance of the proposed method with some popular smoke detection methods on Yuan-videoset test and CVPR-videoset testing datasets. It needs to be explained that the implementation process of these comparison methods in our experiment is designed according to the implementation steps from corresponding references [3,5,11,14,[16][17][18][19]. In order to improve the performance, references [3] and [5] also uses image block approach described in section 3.1, and the feature classification uses a SVM classifier.…”

Section: Performance Comparison (1) Two-stage Smoke Detection Performmentioning

confidence: 99%

“…Vision-based smoke detection technology often uses tranditional image features such as color, texture and shape to detect smoke objects [2][3][4][5][6][7], and the motion features are useful for video smoke detection [8][9][10][11][12]. With the development of deep learning technology, deep features have also been widely used in the field of smoke detection in recent years [13][14][15][16][17][18][19]. Although deep learning methods greatly improve the accuracy of smoke detection, detections of smoke-like objects such as cloud and fog remain erroneous.…”

Section: Introductionmentioning

confidence: 99%

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Video smoke detection with block DNCNN and visual change image

Liu¹,

Cheng²,

Yuan³

et al. 2020

KSII TIIS

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Smoke detection is helpful for early fire detection. With its large coverage area and low cost, vision-based smoke detection technology is the main research direction of outdoor smoke detection. We propose a two-stage smoke detection method combined with block Deep Normalization and Convolutional Neural Network (DNCNN) and visual change image. In the first stage, each suspected smoke region is detected from each frame of the images by using block DNCNN. According to the physical characteristics of smoke diffusion, a concept of visual change image is put forward in this paper, which is constructed by the video motion change state of the suspected smoke regions, and can describe the physical diffusion characteristics of smoke in the time and space domains. In the second stage, the Support Vector Machine (SVM) classifier is used to classify the Histogram of Oriented Gradients (HOG) features of visual change images of the suspected smoke regions, in this way to reduce the false alarm caused by the smoke-like objects such as cloud and fog. Simulation experiments are carried out on two public datasets of smoke. Results show that the accuracy and recall rate of smoke detection are high, and the false alarm rate is much lower than that of other comparison methods.

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Section: Performance Comparison (1) Two-stage Smoke Detection Performmentioning

confidence: 83%

Section: Related Workmentioning

confidence: 99%

Section: Performance Comparison (1) Two-stage Smoke Detection Performmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Video smoke detection with block DNCNN and visual change image

Liu¹,

Cheng²,

Yuan³

et al. 2020

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…Existing methods regarding automatic smoke detection can be itemized into two categories, namely conventional detection approaches based on shallow machine learning [3][4][5], and deep learning methods based on deep neural networks [6]. Conventional detection approaches generally use handcrafted smoke features to train the classifiers (e.g., K-nearest-neighbor (KNN), support vector machine (SVM), AdaBoost) in the training and test scenes.…”

Section: Introductionmentioning

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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Fire and Smoke Image Recognition

Ko,

Hamed Mozaffari,

2024

Digital Innovations in Architecture, Engineering and Construction

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Recurrent convolutional network for video-based smoke detection

Cited by 50 publications

References 26 publications

Video smoke detection with block DNCNN and visual change image

Video smoke detection with block DNCNN and visual change image

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

Fire and Smoke Image Recognition

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