构建无人机拍摄的建筑单元火灾数据集，提升火情检测泛化能力。
Unit: Building Unit Detection Dataset
- 用真实多层场景合成背景，结合运动模糊与亮度调整增强图像真实性。
- 通过大模型生成不同位置的火焰效果，共生成1978张合成图像。
- 适合做火灾早期预警、应急救援等视觉任务的研究者使用。
火灾场景数据集对训练鲁棒的计算机视觉模型至关重要，尤其在火灾早期预警和应急救援任务中。然而，现有火灾相关数据中，针对建筑单元的标注数据严重不足。为解决这一问题，我们提出一个由无人机拍摄的建筑单元标注数据集，采用多种增强技术：使用真实多层场景构建背景，结合运动模糊与亮度调整提升图像真实感，模拟不同条件下的无人机拍摄情况，并利用大模型生成不同位置的火焰效果。该方法生成的合成数据集涵盖广泛的建筑场景，共计1,978张图像。该数据集能有效提升火灾单元检测的泛化能力，在提供多场景、可扩展数据的同时，降低真实火灾数据采集的风险与成本。数据集已开源：https://github.com/boilermakerr/FireUnitData。
原文摘要 · Abstract (English)
Fire scene datasets are crucial for training robust computer vision models, particularly in tasks such as fire early warning and emergency rescue operations. However, among the currently available fire-related data, there is a significant shortage of annotated data specifically targeting building units.To tackle this issue, we introduce an annotated dataset of building units captured by drones, which incorporates multiple enhancement techniques. We construct backgrounds using real multi-story scenes, combine motion blur and brightness adjustment to enhance the authenticity of the captured images, simulate drone shooting conditions under various circumstances, and employ large models to generate fire effects at different locations.The synthetic dataset generated by this method encompasses a wide range of building scenarios, with a total of 1,978 images. This dataset can effectively improve the generalization ability of fire unit detection, providing multi-scenario and scalable data while reducing the risks and costs associated with collecting real fire data. The dataset is available at https://github.com/boilermakerr/FireUnitData.
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