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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Forest Fires Detection using Deep Transfer Learning
Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130832
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 8, 2022.
Abstract: Forests are vital ecosystems composed of various plant and animal species that have evolved over years to coexist. Such ecosystems are often threatened by wildfires that can start either naturally, as a result of lightning strikes, or unintentionally caused by humans. In general, human-caused fires are more severe and expensive to fight because they are frequently located in inaccessible areas. Wildfires can spread quickly and become extremely dangerous, causing damage to homes and facilities, as well as killing people and animals. Early discovery of wildfires is vital to protect lives, property, and resources. Reinforced imaging technologies can play a key role to detect wildfires earlier. By applying deep learning (DL) over a dataset of images (collected using drones, planes, and satellites), we target to automate the forest fire detection. In this paper, we focus on building a DL model specifically to detect wildfires using transfer learning techniques from the best pretrained DL computer vision architectures available nowadays, such as VGG16, VGG19, Inceptionv3, ResNet50, ResNet50V2, InceptionResNetV2, Xception, Dense-Net, MobileNet, MobileNetV2, and NASNetMobile. Our proposed approach attained a detection rate of more than 99.9% over multiple metrics, proving that it could be used in real-world forest fire detection applications.
Keywords: Forest fires; wildfires; deep learning; transfer learning; computer vision; convolutional neural networks (CNN)
Mimoun YANDOUZI, Mounir GRARI, Idriss IDRISSI, Mohammed BOUKABOUS, Omar MOUSSAOUI, Mostafa AZIZI, Kamal GHOUMID and Aissa KERKOUR ELMIAD, “Forest Fires Detection using Deep Transfer Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 13(8), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130832
@article{YANDOUZI2022,
title = {Forest Fires Detection using Deep Transfer Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130832},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130832},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {8},
author = {Mimoun YANDOUZI and Mounir GRARI and Idriss IDRISSI and Mohammed BOUKABOUS and Omar MOUSSAOUI and Mostafa AZIZI and Kamal GHOUMID and Aissa KERKOUR ELMIAD}
}
