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.

用火花检测为主题的合成数据集 FireFly，使用 Unreal Engine 4 生成，旨在克服现有合成资源的缺乏。研究生成了 19,273 帧数据，用于评估 FireFly 在四种常见目标检测模型上的性能，通过半自动标注过程，并在现实野火情景中比较，取得了最高 8.57% 的平均精度改善。