  This research paper addresses the challenge of detecting obscured wildfires
(when the fire flames are covered by trees, smoke, clouds, and other natural
barriers) in real-time using drones equipped only with RGB cameras. We propose
a novel methodology that employs semantic segmentation based on the temporal
analysis of smoke patterns in video sequences. Our approach utilizes an
encoder-decoder architecture based on deep convolutional neural network
architecture with a pre-trained CNN encoder and 3D convolutions for decoding
while using sequential stacking of features to exploit temporal variations. The
predicted fire locations can assist drones in effectively combating forest
fires and pinpoint fire retardant chemical drop on exact flame locations. We
applied our method to a curated dataset derived from the FLAME2 dataset that
includes RGB video along with IR video to determine the ground truth. Our
proposed method has a unique property of detecting obscured fire and achieves a
Dice score of 85.88%, while achieving a high precision of 92.47% and
classification accuracy of 90.67% on test data showing promising results when
inspected visually. Indeed, our method outperforms other methods by a
significant margin in terms of video-level fire classification as we obtained
about 100% accuracy using MobileNet+CBAM as the encoder backbone.
