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 |
DOI | 10.1117/12.2604559 |
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 |
推荐引用方式 GB/T 7714 | Liu, Ruqi,Wu, Siyuan,Lu, Xiaoqiang. Real-time fire detection network for intelligent surveillance systems[C]:SPIE,2021. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Real-time fire detec(3060KB) | 会议论文 | 限制开放 | CC BY-NC-SA | 请求全文 |
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