A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Light-Weight Convolutional Neural Network For Fire Detection
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 human resources.
Light-Weight Convolutional Neural Network For Fire Detection
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 human resources.
Light-Weight Convolutional Neural Network For Fire Detection
Abdel-Zaher, Mohamed (author) / Hisham, Mustafa (author) / Yousri, Retaj (author) / Darweesh, M. Saeed (author)
2021-07-03
19153189 byte
Conference paper
Electronic Resource
English