利用无人机自主路径规划与深度学习进行灾后建筑室内损伤及幸存者检测
Post-disaster building indoor damage and survivor detection using autonomous path planning and deep learning with unmanned aerial vehicles
AI总结：
针对灾后建筑室内搜救需求，提出融合自主导航、深度学习检测与低成本MAV的自主检查方法，并在模拟灾后办公楼中验证其高精度与应用潜力。
摘要：
对地震等自然灾害作出快速响应，是确保民用基础设施安全并最大限度减少人员伤亡的关键因素。传统人工检查劳动密集、耗时，并且可能对检查人员和救援人员构成危险。本文提出一种用于灾后建筑室内场景中结构损伤检查与幸存者检测的自主检查方法，该方法融合了自主导航方法、基于深度学习的损伤与幸存者检测方法，以及一架搭载机载传感器的定制化低成本微型飞行器（MAV）。在一栋模拟灾后办公楼中开展的实验研究表明，所提出的方法体系能够在结构损伤检查和幸存者检测方面实现高精度。总体而言，所提出的检查方法在提升现有灾后建筑人工检查效率方面展现出巨大潜力。
英文摘要：
Rapid response to natural disasters such as earthquakes is a crucial element in ensuring the safety of civil infrastructures and minimizing casualties. Traditional manual inspection is labour-intensive, time-consuming, and can be dangerous for inspectors and rescue workers. This paper proposed an autonomous inspection approach for structural damage inspection and survivor detection in the post-disaster building indoor scenario, which incorporates an autonomous navigation method, deep learning-based damage and survivor detection method, and a customized low-cost micro aerial vehicle (MAV) with onboard sensors. Experimental studies in a pseudo-post-disaster office building have shown the proposed methodology can achieve high accuracy in structural damage inspection and survivor detection. Overall, the proposed inspection approach shows great potential to improve the efficiency of existing manual post-disaster building inspection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
