Real-time fire detection network for intelligent surveillance systems

doi:10.1117/12.2604559

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	Real-time fire detection network for intelligent surveillance systems
	Liu, Ruqi 1,2; Wu, Siyuan 1,2; Lu, Xiaoqiang 2
	2021
会议日期	2021-06-25
会议地点	Liuzhou, China
关键词	Fire detection Surveillance, Yolov5 Single Stage Headless Context Receptive Field Block
卷号	11911
DOI	10.1117/12.2604559
英文摘要	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.
产权排序	1
会议录	2nd International Conference on Computer Vision, Image, and Deep Learning
会议录出版者	SPIE
语种	英语
ISSN号	0277786X;1996756X
ISBN号	9781510646810
内容类型	会议论文
源URL	[http://ir.opt.ac.cn/handle/181661/95376]
专题	西安光学精密机械研究所_光学影像学习与分析中心
作者单位	1.University of Chinese Academy of Sciencea, Beijing; 100049, China 2.Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Shaanxi, Xi'an; 710119, 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]. 见:. Liuzhou, China. 2021-06-25.