Smart City Gnosys

Smart city article details

Title	Image-Based Criticality-Aware Fire Detection
ID_Doc	30247
Authors	Arshad A.; Shamsi J.A.; Khan M.B.; Bawany N.Z.
Year	2024
Published	2024 International Conference on Frontiers of Information Technology, FIT 2024
DOI	http://dx.doi.org/10.1109/FIT63703.2024.10838425
Abstract	Context-aware fire detection is a significant task in the era of new urban monitoring. Severe damage might result from fire events. To minimize the occurrence of these events, timely detection of fire accidents is necessary. Various machine-learning and deep learning methods are used for fire detection. Most of them do not focus on the extent of the fire or the damage it causes. All these methods need the detection of the criticality of the fire event. This work proposes an identification system based on Large Vision Models (LVMs) for fire detection. We proposed an intelligent criticality-aware fire detection system that can detect fire and its varying scales from small to large and generate alarming alerts accordingly. The model integrates advanced computer vision techniques with LVMs to explain the hidden context in a textual format. The system is tested on real-time data collected from surveillance cameras and achieves an accuracy of 86.67% in correctly identifying the context-based criticality of the fire events depicted in the input data. This proposed framework can revolutionize surveillance applications by enhancing security and responding to crucial fire incidents in real-time. The system provides an efficient method for allocating resources and facilitates rapid response to incidents to prioritize activities intelligently. Using this approach, we identified and prioritized fire incidents in the urban environment. © 2024 IEEE.
Author Keywords	computer vision; deep learning; Fire detection; smart city

Similar Articles

Id	Similarity	Authors	Title	Published
3980	0.907	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)
26575	0.895	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)
46920	0.888	Abdali A.M.R.; Ghani R.F.	Robust Real-Time Fire Detector Using Cnn And Lstm	2019 IEEE Student Conference on Research and Development, SCOReD 2019 (2019)
26572	0.886	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)
61276	0.88	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	0.879	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)
8257	0.875	Talaat F.M.; ZainEldin H.	An Improved Fire Detection Approach Based On Yolo-V8 For Smart Cities	Neural Computing and Applications, 35, 28 (2023)
26592	0.872	He Y.; Sahma A.; He X.; Wu R.; Zhang R.	Firenet: A Lightweight And Efficient Multi-Scenario Fire Object Detector	Remote Sensing, 16, 21 (2024)
59290	0.868	Hussain T.; Dai H.; Gueaieb W.; Sicklinger M.; De Masi G.	Uav-Based Multi-Scale Features Fusion Attention For Fire Detection In Smart City Ecosystems	ISC2 2022 - 8th IEEE International Smart Cities Conference (2022)