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Title	Firenet 2.0: Advanced Neural Framework For Smart Fire Detection & Localization
ID_Doc	26589
Authors	Aruthaveni R.M.; Dhivya B.; Hariharan M.; Siva R.
Year	2023
Published	2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings
DOI	http://dx.doi.org/10.1109/ICACRS58579.2023.10404325
Abstract	Fire safety plays an important role in smart environments, and traditional fire detection systems frequently demonstrate limits regarding reaction time and false alarms. The proposed study offers FIREnet 2.0, an innovative fire detection and localization system that harnesses the power of Edge AI and 5G connection. S uch a system is driven by the need for faster and more accurate fire detection in smart cities, industrial settings, and beyond. Existing fire detection systems usually suffer from high false alarm rates, slow reaction times, and restricted scalability. FIREnet 2.0, on the other hand, provides an innovative solution. The proposed study describes the system's novel technique: data collection, Edge AI processing with modified YOLO-based CNNs, 5G data transfer, fire localization, and reaction coordination. Extensive testing, with controlled, real-world fire tests, shows that FIREnet 2.0 delivers high accuracy, lowers false alarms, and considerably shortens reaction times compared to existing systems. The novel method of fire detection has the potential to revolutionize fire safety in smart environments and critical infrastructure, boosting safety and response times. © 2023 IEEE.
Author Keywords	5<sup>th</sup> generation connectivity; Convolutional Neural Networks; Edge Artificial Intelligence; Fire detection; Fire Localization; Smart environments

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