An Improved Object Detection Method Based On Deep Convolution Neural Network For Smoke Detection

Zeng, Junying; Lin, Zuoyong; Qi, Chuanbo; Zhao, Xiaoxiao; Wang, Fan

doi:10.1109/icmlc.2018.8527037

Cited by 22 publications

(8 citation statements)

References 7 publications

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“…Different from the channel attention mechanism of SE [10] , ECA uses adaptive functions to dynamically adjust the size of the convolution kernel to better capture the channel dependencies between different layers, thus improving the performance and accuracy of the model. The calculation formula of adaptive function is shown in (1), where γ=2 and b=1.…”

Section: Weighted Bidirectional Characteristic Pyramid Eca-bifpnmentioning

confidence: 99%

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Forest fire image detection method based on improved CenterNet

Wang

Sun

et al. 2023

Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023)

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In order to quickly and accurately identify forest fires, a forest fire detection method based on CenterNet without anchor frame was proposed. Firstly, lightweight backbone CSP-VoVNet is used to improve the backbone network and improve the detection speed. Secondly, the multi-scale fusion was carried out by introducing the weighted bidirectional characteristic pyramid BiFPN of ECA to improve the detection accuracy of small fireworks. Finally, Smooth L1 was used to regression center point offset to improve model robustness. The experimental results show that the method can accurately identify the small size forest fireworks and realize the early warning effect under the premise of high detection speed.

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Section: Weighted Bidirectional Characteristic Pyramid Eca-bifpnmentioning

confidence: 99%

“…Zeng [1] improved the object detection method based on CNN to detect flame smoke, and achieved good performance in open space, exceeding the detection accuracy of traditional methods. Wang [2] built a fire detection model based on fire color component and introduced flame movement characteristics to reduce the false alarm rate.…”

Section: Introductionmentioning

confidence: 99%

Forest fire image detection method based on improved CenterNet

Wang

Sun

et al. 2023

Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023)

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“…Different fire signatures such a flame, smoke, and heat were used for fire and smoke detection using CNN by different researchers [20][21][22][23][24][25][26]. Some authors extended their work to include the detection of forest fires and enable fast response time for firefighting and the performance of rescue operations.…”

Section: Literature Reviewmentioning

confidence: 99%

Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs)

Sowah

Apeadu

Gatsi

et al. 2020

Journal of Engineering

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This paper presents the design and development of a fuzzy logic-based multisensor fire detection and a web-based notification system with trained convolutional neural networks for both proximity and wide-area fire detection. Until recently, most consumer-grade fire detection systems relied solely on smoke detectors. These offer limited protection due to the type of fire present and the detection technology at use. To solve this problem, we present a multisensor data fusion with convolutional neural network (CNN) fire detection and notification technology. Convolutional Neural Networks are mainstream methods of deep learning due to their ability to perform feature extraction and classification in the same architecture. The system is designed to enable early detection of fire in residential, commercial, and industrial environments by using multiple fire signatures such as flames, smoke, and heat. The incorporation of the convolutional neural networks enables broader coverage of the area of interest, using visuals from surveillance cameras. With access granted to the web-based system, the fire and rescue crew gets notified in real-time with location information. The efficiency of the fire detection and notification system employed by standard fire detectors and the multisensor remote-based notification approach adopted in this paper showed significant improvements with timely fire detection, alerting, and response time for firefighting. The final experimental and performance evaluation results showed that the accuracy rate of CNN was 94% and that of the fuzzy logic unit is 90%.

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“…In practice, these methods may suffer disadvantages such as the dependence of sensors on the working environment, a low detection accuracy, a high level of human involvement, and delayed fire warnings. Thus, stations cannot be left running while completely unattended [3][4][5][6] .…”

Section: Introductionmentioning

confidence: 99%

Video analysis-based intelligent fire warning system for heat exchange stations

Wang,

Zhang,

et al. 2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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In order to solve the problem of fire detection in the heat exchange station, an intelligent fire early warning system based on image analysis is proposed, which realizes the network, digital and intelligent design of video monitoring system. This paper introduces the framework of the platform and the functional structure of each subsystem, gives the overall structure diagram of the system, focuses on the basic algorithm of background modeling, flame segmentation and morphological filtering in the fire identification and analysis module, and puts forward the design scheme and workflow of the alarm and linkage module. Experiments show that the system has low complexity and good real-time performance, which can improve the efficiency of fire emergency command in heat exchange station and meet the actual requirements of heating security.

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An Improved Object Detection Method Based On Deep Convolution Neural Network For Smoke Detection

Cited by 22 publications

References 7 publications

Forest fire image detection method based on improved CenterNet

Forest fire image detection method based on improved CenterNet

Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs)

Video analysis-based intelligent fire warning system for heat exchange stations

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