I’m excited to announce that our publication is submiited in IEEE International Conference of Acoustics, Speech and Signal Processing 2023.

Abstract

There have been many recent developments in the use of Deep Learning Neural Networks for fire detection. In this paper, we explore an early warning system for the detection of forest fires. Due to the lack of sizeable datasets and models tuned for this task, existing methods suffer from missed detection. In this work, we first propose a dataset for the early identification of forest fires through visual analysis. Unlike existing image corpora that contain images of widespread fire, our dataset consists of multiple instances of smoke plumes and fire that indicate the initiation of fire. We obtained this dataset synthetically by utilizing game simulators such as Red Dead Redemption 2. We also combined our dataset with already published images to obtain a more comprehensive set. Finally, we compared image classification and localization methods on the proposed dataset. More specifically we used YOLOv7 (You Only Look Once) and different models of detection transformers