Oral Presentation , Page 99-103 (5)

Volume Title: 2nd IEEE International Conference on Electronic Eng., Faculty of Electronic Eng., Menouf, Egypt, 3-4 July. 2021

¹Mohamed Abdel-Zaher; ¹Mustafa Hisham; ²Retaj Yousri; ³Mohamed Darweesh

¹October University for Modern Sciences and Arts, Giza, Egypt

²Wireless Intelligent Networks Center (WINC), Nile University

³Electronics and Computer Engineering, School of Engineering and Applied Sciences, Nile University, Giza, Egypt

Fire disasters damage the economy across the globe and cause many casualties among civilians and firefighters. In this paper, a deep learning architecture based on the convolutional neural network (CNN) is proposed to detect fires efficiently. We trained the network on 9247, picked high-resolution images containing fire and other ones without any fire, and investigated the effect of CNN depth on its classification accuracy. In this proposed work, we achieved 98% accuracy on the testing set, which is so far better than the previous state-of-the-art and will eventually minimize fire disasters and reduce the damage caused by different human resources.

Convolution Neural Network (CNN); Classification; Fire Detection; Computer Vision; Supervised Learning