Smart City Gnosys

Smart city article details

Title	Robust Real-Time Fire Detector Using Cnn And Lstm
ID_Doc	46920
Authors	Abdali A.M.R.; Ghani R.F.
Year	2019
Published	2019 IEEE Student Conference on Research and Development, SCOReD 2019
DOI	http://dx.doi.org/10.1109/SCORED.2019.8896246
Abstract	The Detection of a fire in surveillance systems is playing a significant role to Reduce material and human losses, the effectiveness of fire detectors measured by the speed of response and the accuracy and the generality over different kinds of video sources with a different format. Several studies worked on fire detection. Also, there is several benchmarking dataset, even though all available datasets are not large enough to build a robust real-world fire detector. In this paper, we proposed a real-time fire detector based on deep-learning, the model consists of Convolutional neural network (CNN) as spatial feature extractor and Long short-term memory (LSTM) as temporal relation learning method with a focus on the three-factor (overall generality - accuracy - fast response time) the proposed model achieved accuracy of 95.39% with a speed of 120 frames/sec based on extended dataset from the available data sets. The accuracy and the speed of the proposed model have been compared to previous works, shows that the proposed model has the highest accuracy and the fastest speed among all the previous works in the field of fire detection. © 2019 IEEE.
Author Keywords	CNN; Deep Learning; Fire Detection; LSTM; Smart Cities

Similar Articles

Id	Similarity	Authors	Title	Published
3980	0.904	Tamilselvi M.; Ramkumar G.; Thandaiah Prabu R.; Anitha G.; Mohanavel V.	A Real-Time Fire Recognition Technique Using A Improved Convolutional Neural Network Method	Proceedings of 8th IEEE International Conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2023 (2023)
26883	0.899	Karthi M.; Priscilla R.; Subhashini G.; Infantia C.N.; Abijith G.R.; Vinisha J.	Forest Fire Detection: A Comparative Analysis Of Deep Learning Algorithms	Proceedings of the International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023 (2023)
30247	0.888	Arshad A.; Shamsi J.A.; Khan M.B.; Bawany N.Z.	Image-Based Criticality-Aware Fire Detection	2024 International Conference on Frontiers of Information Technology, FIT 2024 (2024)
25423	0.887	Saponara S.; Elhanashi A.; Gagliardi A.	Exploiting R-Cnn For Video Smoke/Fire Sensing In Antifire Surveillance Indoor And Outdoor Systems For Smart Cities	Proceedings - 2020 IEEE International Conference on Smart Computing, SMARTCOMP 2020 (2020)
26575	0.881	Avazov, K; Mukhiddinov, M; Makhmudov, F; Cho, YI	Fire Detection Method In Smart City Environments Using A Deep-Learning-Based Approach	ELECTRONICS, 11, 1 (2022)
26572	0.877	Ud D.S.; Muhammad S.; Han F.; Shoaib H.; Ather Iqbal H.M.	Fire Classification Using Attention-Based Deep-Cnn In Digital Images	Proceedings - 2023 10th International Conference on Electrical and Electronics Engineering, ICEEE 2023 (2023)
46921	0.875	Abdali A.-M.R.; Al-Tuma R.F.	Robust Real-Time Violence Detection In Video Using Cnn And Lstm	SCCS 2019 - 2019 2nd Scientific Conference of Computer Sciences (2019)
61276	0.869	Nadeem M.; Dilshad N.; Alghamdi N.S.; Dang L.M.; Song H.-K.; Nam J.; Moon H.	Visual Intelligence In Smart Cities: A Lightweight Deep Learning Model For Fire Detection In An Iot Environment	Smart Cities, 6, 5 (2023)
17904	0.866	Yang W.; Wu Y.; Chow S.K.K.	Deep Learning Method For Real-Time Fire Detection System For Urban Fire Monitoring And Control	International Journal of Computational Intelligence Systems, 17, 1 (2024)
26589	0.862	Aruthaveni R.M.; Dhivya B.; Hariharan M.; Siva R.	Firenet 2.0: Advanced Neural Framework For Smart Fire Detection & Localization	2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings (2023)