用生成式AI合成烟雾数据，提升野火早期检测准确率
Generative AI for Enhanced Wildfire Detection: Bridging the Synthetic-Real Domain Gap
- 用生成模型合成带标注的烟雾图像数据
- 通过风格迁移等技术缩小仿真与真实数据差距
- 适合做遥感图像检测或数据稀缺场景的研究者
野火的早期探测是关键环境挑战，及时识别烟雾羽流对减少大规模破坏至关重要。尽管深度神经网络在定位任务中表现优异，但烟雾检测缺乏大规模标注数据集，限制了其潜力。为此，我们利用生成式AI技术合成一个全面且带标注的烟雾数据集，并探索无监督域自适应方法进行烟雾羽流分割，分析其在弥合仿真与真实数据差距方面的有效性。为进一步提升性能，我们集成风格迁移、生成对抗网络（GANs）和图像抠图等先进生成方法，旨在增强合成数据的真实感，缓解域差异，为更精准、可扩展的野火检测模型铺平道路。
原文摘要 · Abstract (English)
The early detection of wildfires is a critical environmental challenge, with timely identification of smoke plumes being key to mitigating large-scale damage. While deep neural networks have proven highly effective for localization tasks, the scarcity of large, annotated datasets for smoke detection limits their potential. In response, we leverage generative AI techniques to address this data limitation by synthesizing a comprehensive, annotated smoke dataset. We then explore unsupervised domain adaptation methods for smoke plume segmentation, analyzing their effectiveness in closing the gap between synthetic and real-world data. To further refine performance, we integrate advanced generative approaches such as style transfer, Generative Adversarial Networks (GANs), and image matting. These methods aim to enhance the realism of synthetic data and bridge the domain disparity, paving the way for more accurate and scalable wildfire detection models.
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