TITLE:
Optimal Flame Detection of Fires in Videos Based on Deep Learning and the Use of Various Optimizers
AUTHORS:
Tidiane Fofana, Sié Ouattara, Alain Clement
KEYWORDS:
Image Classification, Optimizers, Transfer Learning, VGG16, VGG19
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.11 No.11,
November
30,
2021
ABSTRACT: Deep
learning has recently attracted a lot of attention with the aim of developing a
fast, automatic and accurate system for image identification and
classification. In this work, the focus was on transfer learning and evaluation
of state-of-the-art VGG16 and 19 deep convolutional neural networks for fire
image classification from fire images. In this study, five different approaches
(Adagrad, Adam, AdaMax, Nadam and Rmsprop) based on the gradient descent methods used in
parameter updating were studied. By selecting specific learning rates, training image base proportions, number of recursion
(epochs), the advantages and disadvantages of each approach are compared
with each other in order to achieve the
minimum cost function. The results of the comparison are presented in
the tables. In our experiment, Adam optimizers with the VGG16 architecture with
300 and 500 epochs tend to steadily improve their accuracy with increasing
number of epochs without deteriorating performance. The optimizers were
evaluated on the basis of their AUC of the ROC curve. It achieves a test
accuracy of 96%, which puts it ahead of other architectures.