FireFly A Synthetic Dataset for Ember Detection in Wildfire
Yue Hu Xinan Ye Yifei Liu Souvik Kundu Gourav Datta Srikar Mutnuri Namo Asavisanu Nora Ayanian Konstantinos Psounis Peter Beerel
This paper presents "FireFly", a synthetic dataset for ember detection
created using Unreal Engine 4 (UE4), designed to overcome the current lack of
ember-specific training resources. To create the dataset, we present a tool
that allows the automated generation of the synthetic labeled dataset with
adjustable parameters, enabling data diversity from various environmental
conditions, making the dataset both diverse and customizable based on user
requirements. We generated a total of 19,273 frames that have been used to
evaluate FireFly on four popular object detection models. Further to minimize
human intervention, we leveraged a trained model to create a semi-automatic
labeling process for real-life ember frames. Moreover, we demonstrated an up to
8.57% improvement in mean Average Precision (mAP) in real-world wildfire
scenarios compared to models trained exclusively on a small real dataset.