  This research addresses the pressing challenge of enhancing processing times
and detection capabilities in Unmanned Aerial Vehicle (UAV)/drone imagery for
global wildfire detection, despite limited datasets. Proposing a Segmented
Neural Network (SegNet) selection approach, we focus on reducing feature maps
to boost both time resolution and accuracy significantly advancing processing
speeds and accuracy in real-time wildfire detection. This paper contributes to
increased processing speeds enabling real-time detection capabilities for
wildfire, increased detection accuracy of wildfire, and improved detection
capabilities of early wildfire, through proposing a new direction for image
classification of amorphous objects like fire, water, smoke, etc. Employing
Convolutional Neural Networks (CNNs) for image classification, emphasizing on
the reduction of irrelevant features vital for deep learning processes,
especially in live feed data for fire detection. Amidst the complexity of live
feed data in fire detection, our study emphasizes on image feed, highlighting
the urgency to enhance real-time processing. Our proposed algorithm combats
feature overload through segmentation, addressing challenges arising from
diverse features like objects, colors, and textures. Notably, a delicate
balance of feature map size and dataset adequacy is pivotal. Several research
papers use smaller image sizes, compromising feature richness which
necessitating a new approach. We illuminate the critical role of pixel density
in retaining essential details, especially for early wildfire detection. By
carefully selecting number of filters during training, we underscore the
significance of higher pixel density for proper feature selection. The proposed
SegNet approach is rigorously evaluated using real-world dataset obtained by a
drone flight and compared to state-of-the-art literature.
