Indoor Video Flame Detection Based on Lightweight Convolutional Neural Network

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

doi:10.1134/s1054661820030293

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…With the development of deep learning [2–6], frameworks applying deep convolutional neural networks (CNNs) can detect fires more accurately and efficiently [7–9, 10–13]. Muhammad et al.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic attention network for flame detection

Fang

Zhang

et al. 2023

Electronics Letters

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Recently, flame detection has attracted great attention. However, existing methods have the issues of low detection rates, high false alarm rates, and lack of smoke anti-interference ability. In this letter, a novel dynamic attention-based network (DANet) is proposed for autonomous flame detection in various scenarios. To mitigate the disturbance of smoke in images, a dynamic attention strategy is proposed to discover the potential features among scale-awareness and spatial-awareness. Then, based on dynamic attention module, a decoupled detection head is presented, which can predict category, regression, and object score independently to boost the performance. A self-contained challenging flame dataset, which is multi-scene, multiscale, and multi-interference informative is constructed to evaluate the proposed model and organize the experiments. Extensive ablation and comparison studies on self-labelled dataset reveal the effectiveness of the proposed dynamic attention-based network.

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Section: Introductionmentioning

confidence: 99%

“…With the development of deep learning [2][3][4][5][6], frameworks applying deep convolutional neural networks (CNNs) can detect fires more accurately and efficiently [7][8][9][10][11][12][13]. Muhammad et al propose a computationally efficient CNN structure to localize, and understand the semantic of fire scenes [8].…”

mentioning

confidence: 99%

Dynamic attention network for flame detection

Fang

Zhang

et al. 2023

Electronics Letters

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show abstract

“…Previous fire detection methods based on physical sensors have limitations, including limited use space, high costs, and false positives. Increasing attention has been paid to fire detection algorithms based on images due to their intuitive and real-time characteristics [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

ADFireNet: An Anchor-Free Smoke and Fire Detection Network Based on Deformable Convolution

Liu

2023

Sensors

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In this paper, we propose an anchor-free smoke and fire detection network, ADFireNet, based on deformable convolution. The proposed ADFireNet network is composed of three parts: The backbone network is responsible for feature extraction of input images, which is composed of ResNet added to deformable convolution. The neck network, which is responsible for multi-scale detection, is composed of the feature pyramid network. The head network outputs results and adopts pseudo intersection over union combined with anchor-free network structure. The head network consists of two full convolutional subnetworks: the first is the classification sub-network, which outputs a classification confidence score, and the second is the regression sub-network, which predicts the parameters of bounding boxes. The deformable convolution (DCN) added to the backbone network enhances the shape feature extraction capability for fire and smoke, and the pseudo intersection over union (pseudo-IoU) added to the head network solves the label assignment problem that exists in anchor-free object detection networks. The proposed ADFireNet is evaluated using the fire smoke dataset. The experimental results show that ADFireNet has higher accuracy and faster detection speeds compared with other methods. Ablation studies have demonstrated the effectiveness of DCN and pseudo IoU.

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“…[22] proposed an attention enhanced bidirectional long short-term memory network (ABi-LSTM) for smoke identification of forest fires in video; Pundir and Raman [23] proposed a robust smoke detection method based on dual deep learning framework, the first deep learning framework extracted image-based features from smoke patches, and the second deep learning framework was used to extract motion-based features, which were then input into CNN classifier to complete classification; Yang et al. [24] proposed a neural network model combining lightweight CNN and SRU, which could reduce the influence of strong interference, such as bright light flicker or high brightness background on single-frame fire image recognition. Bilinear convolutional neural network (B-CNN) [25] is a deep learning model based on weakly supervised learning.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Multisource Bilinear Convolutional Neural Network Model for Flame Image Identification of Coal Mine

Zhang

Zhu

et al. 2022

IEEE Access

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Underground fire monitoring is an important tool to improve coal mine production safety. In this paper, a multi-source information identification method based on bilinear convolutional neural network (B-CNN) is proposed, which consists of construction of multi-source image acquisition system, B-CNN and integrated decision making based on multi-source B-CNN. Aiming at the problem that Softmax loss function based on the gradient descent in B-CNN is easy falling into the local optimum, an improved Grasshopper Optimization Algorithm (GOA) is proposed to optimally selected two parameters of W and θ; the method of initial solution generation based on sine mapping and the method of accepting bad solution with certain probability are respectively adopted. In order to solve high computational complexity in the stage of model training and integrated recognition by multi-source B-CNN, an image feature preprocessing method is proposed in this paper. Several feature vectors of color feature, shape feature and texture feature of the collected image are extracted and used as input vectors of B-CNN to complete model training and integrated recognition. In simulation experiments, firstly, four Benchmark functions are used to verify the performance of the improved GOA; then, by scaling, expanding and rotating the image to simulate the results of image acquisition at multiple positions and angles, different information sources can be formed to complete the integrated recognition by B-CNN. Three performance indexes of Accuracy, Precision and Recall are used to evaluate the simulation result of different comparative models, which show that the proposed method has better recognition effects.INDEX TERMS Bilinear convolutional neural network, coal mine, data modeling, feature extraction, image processing.

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Indoor Video Flame Detection Based on Lightweight Convolutional Neural Network

Cited by 7 publications

References 18 publications

Dynamic attention network for flame detection

Dynamic attention network for flame detection

ADFireNet: An Anchor-Free Smoke and Fire Detection Network Based on Deformable Convolution

An Optimized Multisource Bilinear Convolutional Neural Network Model for Flame Image Identification of Coal Mine

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