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Real-time fire detection network for intelligent surveillance systems
Liu, Ruqi1,2; Wu, Siyuan1,2; Lu, Xiaoqiang1
2021
会议名称2nd International Conference on Computer Vision, Image, and Deep Learning
会议录名称2nd International Conference on Computer Vision, Image, and Deep Learning
卷号11911
会议日期2021-06-25
会议地点Liuzhou, China
出版者SPIE
产权排序1
摘要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. © 2021 SPIE.
关键词Fire detection Surveillance, Yolov5 Single Stage Headless Context Receptive Field Block
作者部门光谱成像技术研究室
DOI10.1117/12.2604559
收录类别EI
ISBN号9781510646810
语种英语
ISSN号0277786X;1996756X
EI入藏号20214511113416
引用统计
文献类型会议论文
条目标识符http://ir.opt.ac.cn/handle/181661/95376
专题光谱成像技术研究室
作者单位1.Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Shaanxi, Xi'an; 710119, China;
2.University of Chinese Academy of Sciencea, Beijing; 100049, China
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GB/T 7714
Liu, Ruqi,Wu, Siyuan,Lu, Xiaoqiang. Real-time fire detection network for intelligent surveillance systems[C]:SPIE,2021.
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