Early detection of wildfires is essential to prevent large-scale fires resulting in extensive environmental, structural, and societal damage. Uncrewed aerial vehicles (UAVs) can cover large remote areas effectively with quick deployment requiring minimal infrastructure and equipping them with small cameras and computers enables autonomous real-time detection. In remote areas, however, the UAVs are limited to on-board computing for detection due to the lack of high-bandwidth mobile networks. This limits the detection to methods which are light enough for the on-board computer alone. For accurate camera-based localisation, segmentation of the detected smoke is essential but training data for deep learning-based wildfire smoke segmentation is limited. This study shows how small specialised segmentation models can be trained using only bounding box labels, leveraging zero-shot foundation model supervision. The method offers the advantages of needing only fairly easily obtainable bounding box labels and requiring training solely for the smaller student network. The proposed method achieved 63.3% mIoU on a manually annotated and diverse wildfire dataset. The used model can perform in real-time at ~25 fps with a UAV-carried NVIDIA Jetson Orin NX computer while reliably recognising smoke, demonstrated at real-world forest burning events. Code is available at https://gitlab.com/fgi_nls/public/wildfire-real-time-segmentation

翻译：野火的早期检测对于防止大规模火灾造成广泛的环境、结构和社会损害至关重要。无人驾驶飞行器（UAV）能够以快速部署的方式有效覆盖广阔的偏远区域，且所需基础设施极少，为其配备小型摄像头和计算机即可实现自主实时检测。然而，在偏远地区，由于缺乏高带宽移动网络，无人机仅限于依赖机载计算进行检测。这限制了检测方法必须足够轻量，以便仅凭机载计算机即可运行。对于基于摄像头的精确定位而言，对检测到的烟雾进行分割至关重要，但基于深度学习的野火烟雾分割训练数据有限。本研究展示了如何仅利用边界框标签，借助零样本基础模型的监督来训练小型专用分割模型。该方法具有仅需相对容易获取的边界框标签，且仅需对较小的学生网络进行训练的优势。所提出的方法在手动标注且多样化的野火数据集上达到了63.3%的mIoU。所使用的模型能够在无人机搭载的NVIDIA Jetson Orin NX计算机上以约25 fps的速度实时运行，并在真实森林燃烧事件中可靠地识别烟雾。代码可在 https://gitlab.com/fgi_nls/public/wildfire-real-time-segmentation 获取。