我国云南省自然灾害频发，给人们造成了巨大生命财产损失。为此本文基于无人机高分遥感图像和深度学习的目标检测技术快速定位自然灾害中损坏建筑物的位置，为救灾救援提供助力。然而，目前损坏建筑物检测领域仍存在一些挑战：如目前公开的震灾损坏建筑物的高分辨率数据较少或费用昂贵；待检测损坏建筑物与背景及其他目标特征差异小而错检的问题。针对以上问题，本文构建了基于无人机遥感图像的大规模高分辨率的震灾损坏建筑物数据集，其主要在云南省大理白族自治州漾濞彝族自治县灾区收集4598张遥感图像，并对目标建筑物进行了多种形式标注；在检测算法上，本文提出了震灾损坏建筑物实时检测模型，其中包含目标特征对齐模块，特征差异计算模块和目标边界约束的位置框检测模块。本文提出的模型在震灾建筑物检测数据集上达到了86%的精度，同时也在不同地点的实际场景下进行了验证并得到较好的效果。

Natural disasters occur frequently in Yunnan, China, causing huge losses of life and property. For this reason, this paper uses the object detection technology of deep learning based on remote sensing images to quickly search for the location of damaged buildings caused by natural disasters, and then provide assistance for disaster relief. However, there are still some challenges in the field of damaged building detection, such as the less data of earthquake-damaged buildings and the weak features of the target to be detected. Therefore, this paper constructs a UAV remote sensing images-based large-scale high-resolution earthquake-damaged buildings database (UEDB). It mainly collected 4,598 remote sensing images in the disaster area of Yangbi Yi Autonomous County, Dali Bai Autonomous Prefecture, Yunnan Province. And this dataset contains 76,012 building instances, and each instance is labeled in three formats: object location box label, object segmentation label, and object boundary label. Aiming at the current problems, this paper proposes an earthquake-damaged buildings real-time detection model (EDBRDM), which includes an object feature alignment module (OFAM), a feature difference calculation module (FDCM) and an object boundary constraint based-position box detection module (OBCPB). The proposed model achieves an accuracy of 86% on the test dataset of UEDB, and it has also been verified in actual scenes at different locations and achieved well results.