An efficient fire detection algorithm based on multi‐scale convolutional neural network
Summary Video fire detection (VFD) technology has shown a broad application prospect with the popularization of camera monitoring systems. Since the initial stage is the best time for firefighting, it's crucial to develop a robust algorithm for early warning. In this paper, an efficient VFD fus...
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| Published in | Fire and materials Vol. 46; no. 7; pp. 981 - 992 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Bognor Regis
Wiley Subscription Services, Inc
01.11.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0308-0501 1099-1018 |
| DOI | 10.1002/fam.3045 |
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| Abstract | Summary
Video fire detection (VFD) technology has shown a broad application prospect with the popularization of camera monitoring systems. Since the initial stage is the best time for firefighting, it's crucial to develop a robust algorithm for early warning. In this paper, an efficient VFD fusion algorithm is presented. First, the fire candidate areas (FCA) are located quickly based on low‐level visual features to guarantee well timeliness. Furthermore, a multi‐scale convolutional neural network with spatial pyramid pooling is built and trained on the dedicated flame data set without the requirement of sample labeling. In this case, FCA with different aspect ratios and scales can be accurately identified. The method is fully tested on various databases of arbitrary‐sized images and videos. Experimental results show that the proposed fusion algorithm, in addition to improving the detection efficiency, also ensures the accurate identification of flames on different scales. |
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| AbstractList | Summary
Video fire detection (VFD) technology has shown a broad application prospect with the popularization of camera monitoring systems. Since the initial stage is the best time for firefighting, it's crucial to develop a robust algorithm for early warning. In this paper, an efficient VFD fusion algorithm is presented. First, the fire candidate areas (FCA) are located quickly based on low‐level visual features to guarantee well timeliness. Furthermore, a multi‐scale convolutional neural network with spatial pyramid pooling is built and trained on the dedicated flame data set without the requirement of sample labeling. In this case, FCA with different aspect ratios and scales can be accurately identified. The method is fully tested on various databases of arbitrary‐sized images and videos. Experimental results show that the proposed fusion algorithm, in addition to improving the detection efficiency, also ensures the accurate identification of flames on different scales. Video fire detection (VFD) technology has shown a broad application prospect with the popularization of camera monitoring systems. Since the initial stage is the best time for firefighting, it's crucial to develop a robust algorithm for early warning. In this paper, an efficient VFD fusion algorithm is presented. First, the fire candidate areas (FCA) are located quickly based on low‐level visual features to guarantee well timeliness. Furthermore, a multi‐scale convolutional neural network with spatial pyramid pooling is built and trained on the dedicated flame data set without the requirement of sample labeling. In this case, FCA with different aspect ratios and scales can be accurately identified. The method is fully tested on various databases of arbitrary‐sized images and videos. Experimental results show that the proposed fusion algorithm, in addition to improving the detection efficiency, also ensures the accurate identification of flames on different scales. |
| Author | Gao, Yunji Liu, Yu Yang, Kai Cheng, Yanying Bai, Hui Chen, Ke Mou, Chunjie Zhang, Yuchun |
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| Cites_doi | 10.1016/j.ijleo.2015.05.082 10.1016/j.firesaf.2010.03.001 10.1016/j.csite.2021.101133 10.1109/ICIP.2004.1421401 10.1109/TPAMI.2015.2389824 10.1016/j.firesaf.2008.06.009 10.1007/s10694-019-00832-w 10.1109/5.726791 10.1016/j.firesaf.2015.11.015 10.1109/SIBGRAPI.2015.19 10.1088/0957-0233/24/7/075403 10.1002/fam.2724 10.1016/j.proeng.2013.08.140 10.1016/j.neucom.2017.04.083 10.1016/j.firesaf.2015.03.001 10.1109/TSMC.2018.2830099 10.1007/s11042-017-5090-2 10.1145/3065386 10.3390/rs9080848 10.3390/fi10120115 10.1016/j.firesaf.2008.05.005 10.1016/j.patrec.2005.06.015 10.2298/TSCI100927021B 10.1007/s11042-015-2990-x 10.1016/j.firesaf.2006.02.001 10.1002/0470091150.ch11 10.1016/j.eswa.2019.04.019 10.1016/j.buildenv.2009.10.017 |
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Video fire detection (VFD) technology has shown a broad application prospect with the popularization of camera monitoring systems. Since the initial... Video fire detection (VFD) technology has shown a broad application prospect with the popularization of camera monitoring systems. Since the initial stage is... |
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| SubjectTerms | Algorithms Artificial neural networks Aspect ratio Fire detection Fire fighting multi‐scale convolutional neural network Neural networks rapid fire location scene recognition special data set for fire video fire detection |
| Title | An efficient fire detection algorithm based on multi‐scale convolutional neural network |
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