Abstract. Deep learning methods driven by in situ video and remote sensing images have been used in fire detection. The performance and generalization of fire detection models, however, are restricted by the limited number and modality of fire detection training datasets. A large-scale fire detection benchmark dataset covering complex and varied fire scenarios is urgently needed. This work constructs a 100,000-level Flame and Smoke Detection Dataset (FASDD) based on multi-source heterogeneous flame and smoke images. To the best of our knowledge, FASDD is currently the most versatile and comprehensive dataset for fire detection. It provides a challenging benchmark to drive the continuous evolution of fire detection models. Additionally, we formulate a unified workflow for preprocessing, annotation and quality control of fire samples. Meanwhile, out-of-the-box annotations are published in four different formats for training deep learning models. Deep learning models trained on FASDD demonstrate the potential value and challenges of our dataset in fire detection and localization. Extensive performance evaluations based on classical methods show that most of the models trained on FASDD can achieve satisfactory fire detection results, and especially YOLOv5x achieves nearly 80 % mAP@0.5 accuracy on heterogeneous images spanning two domains of computer vision and remote sensing. And the application in wildfire location demonstrates that deep learning models trained on our dataset can be used in recognizing and monitoring forest fires. It can be deployed simultaneously on watchtowers, drones and optical satellites to build a satellite-ground cooperative observation network, which can provide an important reference for large-scale fire suppression, victim escape, firefighter rescue and government decision-making. The dataset is available from the Science Data Bank website at https://doi.org/10.57760/sciencedb.j00104.00103 (Wang et al., 2022).

中文翻译：

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FASDD：用于火灾探测深度学习的开放式 100,000 级火焰和烟雾探测数据集

摘要。由现场视频和遥感图像驱动的深度学习方法已被用于火灾探测。然而，火灾探测模型的性能和泛化受到有限数量和模式的火灾探测训练数据集的限制。迫切需要一个涵盖复杂多变火灾场景的大规模火灾探测基准数据集。这项工作基于多源异构火焰和烟雾图像构建了一个 100,000 级的火焰和烟雾检测数据集 (FASDD)。据我们所知，FASDD 是目前最通用、最全面的火灾探测数据集。它提供了一个具有挑战性的基准来推动火灾探测模型的不断发展。此外，我们制定了统一的火样预处理、注释和质量控制工作流程。同时，开箱即用的注释以四种不同的格式发布，用于训练深度学习模型。在 FASDD 上训练的深度学习模型展示了我们的数据集在火灾探测和定位方面的潜在价值和挑战。基于经典方法的广泛性能评估表明，大多数在 FASDD 上训练的模型都能取得令人满意的火灾检测结果，尤其是 YOLOv5x 在跨越计算机视觉和遥感两个领域的异构图像上实现了近 80% 的 mAP@0.5 准确率。在野火定位中的应用表明，在我们的数据集上训练的深度学习模型可用于识别和监测森林火灾。可同时部署在瞭望塔、无人机和光学卫星上，构建星地协同观测网络，可为大范围灭火、受难者逃生、消防员救援和政府决策提供重要参考。该数据集可从科学数据库网站 https://doi.org/10.57760/sciencedb.j00104.00103 获取（Wang 等人，2022 年）。