FireFly A Synthetic Dataset for Ember Detection in Wildfire
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 32 references that are in the map. First posted on arXiv 6 Aug 2023.
Builds on
6 of 32 references.
References · 6
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2020End-to-End Object Detection with Transformerscited by 7,414
- 2019MMDetection: Open MMLab Detection Toolbox and Benchmarkcited by 1,385
- 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
- 2020Sparse R-CNN: End-to-End Object Detection with Learnable Proposalscited by 495
- 2020A review of machine learning applications in wildfire science and managementcited by 65
Built on by
Citing papers per year: 2025: 1.
1 paper.