Automating Building Damage Reconnaissance to Optimize Drone Mission Planning for Disaster Response
Automating Building Damage Reconnaissance to Optimize Drone Mission Planning for Disaster Response
复制标题
DOI：
10.1061/(asce)cp.1943-5487.0001061
复制
发表时间：
2023-05
期刊：
影响因子：
--
通讯作者：
Da Hu;Shuai Li;Jing Du;Jiannan Cai
中科院分区：
文献类型：
--
作者：
Da Hu;Shuai Li;Jing Du;Jiannan Cai
建筑物损坏的快速侦察对于灾害响应和恢复至关重要。无人机已被用来收集受影响地区的空中图像，以评估建筑物的损坏。然而，有两个挑战。首先，处理许多航空图像以基于一致的标准检测和分类建筑物损坏仍然是费力和复杂的，需要一种新的自动化解决方案来实现准确的建筑物损坏检测和分类。其次，灾害响应期间的无人机操作主要依赖于人类操作员的经验，很少使用获得的建筑物损坏信息来优化无人机使命规划。因此，本研究提出一种新的方法，自动化建筑物损坏侦察与无人机使命规划灾害响应操作。具体来说，开发了一种深度学习方法，使用由24，496个不同的建筑物损坏实例组成的新标记数据集来检测和分类建筑物损坏。这种深度学习方法得到了验证，达到了71.9%的平均精度。此外，建筑物损坏信息被建模并集成到使命规划中，以优化无人机的任务分配和路线计算。田纳西州的龙卷风灾害被用来作为一个案例研究，定量评估这种方法。本研究的结论是，可以使用从深度学习方法获得的准确建筑物损坏信息来增强灾害响应期间的最佳无人机使命规划。
Rapid reconnaissance of building damage is critical for disaster response and recovery. Drones have been utilized to collect aerial images of affected areas in order to assess building damage. However, there are two challenges. First, processing many aerial images to detect and classify building damage based on a consistent standard remains laborious and complex, necessitating a new automated solution to achieve accurate building damage detection and classification. Second, drone operations during disaster response rely primarily on human operators’ experience and seldom use the obtained building damage information to optimize drone mission planning. Therefore, this study proposes a new method, which automates building damage reconnaissance with drone mission planning for disaster response operations. Specifically, a deep learning method is developed to detect and classify building damages using a newly labeled dataset consisting of 24,496 distinct instances of building damage. This deep learning method is validated, achieving 71.9% mean average precision. In addition, building damage information is modeled and integrated into mission planning, in order to optimize drones’ task assignments and route calculations. A tornado disaster in Tennessee is used as a case study to quantitatively evaluate this methodology. The present study concludes that optimal drone mission planning during disaster response can be augmented using accurate building damage information acquired from deep learning methods.