Abstract:
Approximately 80% of the total organisms on Earth rely on trees for sustenance and shelter.
Homo sapiens mostly inhabited forested environments throughout their existence. Wildfires
have the potential to do harm to forests. Wildfires are an inevitable consequence of the presence
of trees. Uncontrolled Wildfires occur in trees above a height of 1.8 meters (6 feet). In recent
years, there has been a significant surge in the number of academics who are interested in using
computer vision and image processing techniques to detect flames in images. This study
examines several deep learning algorithms used in the detection of Wildfires and does a
comparative analysis among them. I collect the dataset from online which is analyzed using
four different deep learning models: DenseNet201, MobileNetV2, InceptionV3, VGG16. It
contains 18,415 images divided into four categories: fire, non-fire, smoke, and fog. The
experimental results showed that wildfire predictions have a high level of accuracy, with
VGG16 achieving the highest accuracy rate of 97%.
