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• W3202542868 abstract "Based on the concept of deep learning, the proposed convolutional neural networks (CNNs) processing of extracted image features has been recently applied to tackle early fire detection during surveillance. However, such methods generally need more computational time and memory and seldom take smoke that always produced before fires into consideration, which results in poor detection speed and accuracy relatively. In this paper, we propose a novel imagebased fire and smoke detection network. Inspired by Yolov5 architecture, considering the untargeted feature extraction capability and limited receptive fields of Yolov5, the SSHC (Single Stage Headless Context) module is added to the backbone layer to enhance the feature extraction of flames and smoke. The RFB (Receptive Field Block) module is added to the fusion layer to increase the receptive field of our network. Not only does our network detect fire and smoke well in different fire scenes, different shooting angles, and different lighting conditions, but also achieves a speed of 83 FPS, meeting the real-time detection requirements in the detection speed. Meanwhile, we have built a high quality, constructed by collecting from real scenes and annotated by strict and reasonable rules dataset for fire and smoke detection to verify the superiority of our network. Our proposed network achieves 97.2% accuracy for fire detection, 92.4% accuracy for smoke detection. Experimental results on benchmark fire-smoke datasets reveal the effectiveness of the proposed framework and validate its suitability for fire and smoke detection in surveillance systems compared to state-of-the-art methods." @default.
• W3202542868 created "2021-10-11" @default.
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• W3202542868 date "2021-10-05" @default.
• W3202542868 modified "2023-09-26" @default.
• W3202542868 title "Real-time fire detection network for intelligent surveillance systems" @default.
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• W3202542868 doi "https://doi.org/10.1117/12.2604559" @default.
• W3202542868 hasPublicationYear "2021" @default.
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