用游戏生成数据提升森林火灾早期烟雾检测精度
Exploring State-of-the-art models for Early Detection of Forest Fires
- 用游戏模拟生成早期火灾烟雾图像，弥补真实数据不足
- 在自建数据集上，YOLOv7 和检测变压器模型表现优于传统方法
- 适合从事遥感监测、灾害预警的科研与工程人员参考
近年来深度学习在火灾检测中取得进展。本文提出一种森林火灾早期预警系统，针对现有方法因缺乏大规模专用数据集导致漏检的问题，首次构建了一个基于视觉分析的早期火灾检测数据集。该数据集聚焦火灾初起阶段的烟雾和火苗，通过游戏模拟器《荒野大镖客2》合成生成，并融合已有公开图像以增强多样性。在该数据集上，我们对比了图像分类与定位方法，具体评估了YOLOv7及多种检测变压器（Detection Transformer）模型的表现。
原文摘要 · Abstract (English)
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 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 early identification of forest fires through visual analysis. Unlike existing image corpuses that contain images of wide-spread fire, our dataset consists of multiple instances of smoke plumes and fire that indicates the initiation of fire. We obtained this dataset synthetically by utilising 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 localisation methods on the proposed dataset. More specifically we used YOLOv7 (You Only Look Once) and different models of detection transformer.
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