eprintid: 18948 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/01/89/48 datestamp: 2024-06-04 14:11:24 lastmod: 2024-06-04 14:11:24 status_changed: 2024-06-04 14:04:29 type: conference_item metadata_visibility: show creators_name: Omar, M.B. creators_name: Ibrahim, R. creators_name: Bingi, K. creators_name: Haikal Wan Mohd Nadzri, W.M. creators_name: Faqih, M. title: Real-Time UAV System Integration for Fire Detection and Classification ispublished: pub keywords: Aircraft detection; Antennas; Convolution; Fires; Image classification; Smoke; Smoke detectors; Unmanned aerial vehicles (UAV), Aerial vehicle; Convoluted neural network; Fire classification; Fire detection; Fire detection systems; Images processing; Neural-networks; Real- time; Unmanned aerial vehicle; Unmanned aerial vehicle systems, Neural networks note: cited By 1; Conference of 2023 IEEE International Conference on Computing, ICOCO 2023 ; Conference Date: 9 October 2023 Through 12 October 2023; Conference Code:196872 abstract: Early and efficient smoke/fire detection is vital in ensuring the quick arrival and action of the emergency response team. This is to ensure minimal damage is taken by the facility. Therefore, it is important to acknowledge the limits of conventional methods of fire detection such as smoke detector and stationary optic sensors. This includes constraints in providing sufficient data in terms of size and location of fire, the need for smoke particles to reach a certain concentration threshold and sensitivity drop due to the debris accumulation. Hence, the integration of artificial intelligence towards fire detection systems in Unmanned Aerial Vehicles can be used to overcome the flaws of current fire detection systems. Therefore, this paper proposes the Convoluted Neural Network (CNN) model for fire detection and classification with evaluation of processing time, classification accuracy and real time integration to UAV. The study shows that CNN was able to achieve a high prediction accuracy of 93.5. © 2023 IEEE. date: 2023 official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184854851&doi=10.1109%2fICOCO59262.2023.10397888&partnerID=40&md5=14a627f954217a1cd1de4bd36b526805 id_number: 10.1109/ICOCO59262.2023.10397888 full_text_status: none publication: 2023 IEEE International Conference on Computing, ICOCO 2023 pagerange: 237-241 refereed: TRUE citation: Omar, M.B. and Ibrahim, R. and Bingi, K. and Haikal Wan Mohd Nadzri, W.M. and Faqih, M. (2023) Real-Time UAV System Integration for Fire Detection and Classification. In: UNSPECIFIED.