Study of Region Convolutional Neural Network Deep Learning for Fire Accident Detection - Details

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EI Scopus

Abstract：

Fires　accident　is　one　of　the　disasters　which　take　human　life,　infrastructure　destruction　due　to　its　violence　or　to　the　delay　for　the　rescue.　Object　detection　is　one　of　the　popular　topics　in　recent　years,　which　can　play　the　robust　impact　for　detecting　fire　and　more　efficient　to　provide　information　to　this　disaster.　However,　this　study　presents　the　fire　detection　processed　using　region　convolution　neural　network.　We　will　train　images　of　different　objects　in　fire　using　ground　truth　labeling.　After　labeling　images　and　determining　the　region　of　interest　(ROI),　the　features　are　extracted　from　training　data,　and　the　detector　will　be　trained　and　will　work　to　each　and　image　of　fire.　To　validate　the　effectiveness　of　this　system　the　algorithm　demonstrates　images　taken　from　our　dataset.　©　2021,　The　Editor(s)　(if　applicable)　and　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.

Keyword：

Accidents Convolution Convolutional neural networks Deep learning Disasters Fires Image segmentation Intelligent systems Object detection

Community：

[ 1 ] [Jean dAmour, Ntawiheba]School of Information Science and Engineering, Fuzhou University, Fujian University of Technology, No. 33 Xuefu South Road, New District, Fuzhou; Fujian; 350118, China
[ 2 ] [Jean dAmour, Ntawiheba]Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China
[ 3 ] [Chang, Kuo-Chi]School of Information Science and Engineering, Fuzhou University, Fujian University of Technology, No. 33 Xuefu South Road, New District, Fuzhou; Fujian; 350118, China
[ 4 ] [Chang, Kuo-Chi]Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China
[ 5 ] [Chang, Kuo-Chi]College of Mechanical & Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan
[ 6 ] [Li, Pei-Qiang]School of Information Science and Engineering, Fuzhou University, Fujian University of Technology, No. 33 Xuefu South Road, New District, Fuzhou; Fujian; 350118, China
[ 7 ] [Zhou, Yu-Wen]School of Information Science and Engineering, Fuzhou University, Fujian University of Technology, No. 33 Xuefu South Road, New District, Fuzhou; Fujian; 350118, China
[ 8 ] [Zhou, Yu-Wen]Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China
[ 9 ] [Wang, Hsiao-Chuan]Institute of Environmental Engineering, National Taiwan University, Taipei, Taiwan
[ 10 ] [Lin, Yuh-Chung]School of Information Science and Engineering, Fuzhou University, Fujian University of Technology, No. 33 Xuefu South Road, New District, Fuzhou; Fujian; 350118, China
[ 11 ] [Lin, Yuh-Chung]Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China
[ 12 ] [Chu, Kai-Chun]Department of Business Management, Fujian University of Technology, Fuzhou, China
[ 13 ] [Hsu, Tsui-Lien]Institute of Construction Engineering and Management, National Central University, Taoyuan, Taiwan

Reprint 's Address：

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[chang, kuo-chi]college of mechanical & electrical engineering, national taipei university of technology, taipei, taiwan;;[chang, kuo-chi]school of information science and engineering, fuzhou university, fujian university of technology, no. 33 xuefu south road, new district, fuzhou; fujian; 350118, china;;[chang, kuo-chi]fujian provincial key laboratory of big data mining and applications, fujian university of technology, fuzhou, china

Email：

albertchangxuite@gmail.com

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