TY  - CONF
N2  - 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.
N1  - 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
KW  - Aircraft detection; Antennas; Convolution; Fires; Image classification; Smoke; Smoke detectors; Unmanned aerial vehicles (UAV)
KW  -  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
KW  -  Neural networks
TI  - Real-Time UAV System Integration for Fire Detection and Classification
ID  - scholars18948
SP  - 237
AV  - none
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184854851&doi=10.1109%2fICOCO59262.2023.10397888&partnerID=40&md5=14a627f954217a1cd1de4bd36b526805
A1  - Omar, M.B.
A1  - Ibrahim, R.
A1  - Bingi, K.
A1  - Haikal Wan Mohd Nadzri, W.M.
A1  - Faqih, M.
EP  - 241
Y1  - 2023///
ER  -