论文标题
MSNET：空中视频中的自然灾害损害评估的多级实例细分网络
MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos
论文作者
论文摘要
在本文中，我们研究了通过空中视频分析在自然灾害，洪水或火灾等自然灾害之后有效评估建筑物损害的问题。我们做出了两个主要贡献。第一个贡献是一个新的数据集，该数据集由来自社交媒体的用户生成的空中视频组成，并注释了实例级别的建筑物损坏口罩。这为使用空中视频评估建筑物损失的模型进行定量评估提供了第一个基准测试。第二个贡献是一种新的模型，即MSNET，它包含新型的区域建议网络设计和无监督的评分精炼网络，用于在边界框和掩护分支中置信度得分校准。我们表明，与数据集中的先前方法相比，我们的模型可实现最新的结果。我们将发布我们的数据，模型和代码。
In this paper, we study the problem of efficiently assessing building damage after natural disasters like hurricanes, floods or fires, through aerial video analysis. We make two main contributions. The first contribution is a new dataset, consisting of user-generated aerial videos from social media with annotations of instance-level building damage masks. This provides the first benchmark for quantitative evaluation of models to assess building damage using aerial videos. The second contribution is a new model, namely MSNet, which contains novel region proposal network designs and an unsupervised score refinement network for confidence score calibration in both bounding box and mask branches. We show that our model achieves state-of-the-art results compared to previous methods in our dataset. We will release our data, models and code.
