无人机自主巡检+深度学习，快速定位灾后建筑损伤与幸存者
Post-disaster building indoor damage and survivor detection using autonomous path planning and deep learning with unmanned aerial vehicles
- 基于自主路径规划的无人机在室内自动巡检
- 实现高精度建筑损伤与幸存者检测
- 低成本微型无人机适配灾后复杂环境
地震等自然灾害发生后，快速响应对保障基础设施安全、减少伤亡至关重要。传统人工巡查耗时费力且存在安全隐患。本文提出一种面向灾后建筑室内的自主巡检方法，结合自主导航、基于深度学习的损伤与幸存者检测技术，以及搭载机载传感器的定制化低成本微型无人机（MAV）。在模拟灾后办公建筑中的实验表明，该方法在结构损伤检测和幸存者识别方面均取得高准确率。整体而言，该方法显著提升了现有灾后人工巡查的效率，具有广泛应用潜力。
原文摘要 · Abstract (English)
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.
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