Autonomous drones will play an essential role in human-machine teaming in future search and rescue (SAR) missions. We present a prototype that finds people fully autonomously in densely occluded forests. In the course of 17 field experiments conducted over various forest types and under different flying conditions, our drone found, in total, 38 of 42 hidden persons. For experiments with predefined flight paths, the average precision was 86%, and we found 30 of 34 cases. For adaptive sampling experiments (where potential findings are double-checked on the basis of initial classification confidences), all eight hidden persons were found, leading to an average precision of 100%, whereas classification confidence was increased on average by 15%. Thermal image processing, classification, and dynamic flight path adaptation are computed on-board in real time and while flying. We show that deep learning–based person classification is unaffected by sparse and error-prone sampling within straight flight path segments. This finding allows search missions to be substantially shortened and reduces the image complexity to 1/10th when compared with previous approaches. The goal of our adaptive online sampling technique is to find people as reliably and quickly as possible, which is essential in time-critical applications, such as SAR. Our drone enables SAR operations in remote areas without stable network coverage, because it transmits to the rescue team only classification results that indicate detections and can thus operate with intermittent minimal-bandwidth connections (e.g., by satellite). Once received, these results can be visually enhanced for interpretation on remote mobile devices.

自主无人机将在未来搜索和救援 (SAR) 任务中的人机协作中发挥重要作用。我们提出了一个原型，可以在密集的封闭森林中完全自主地找到人。在对各种森林类型和不同飞行条件下进行的 17 次实地试验过程中，我们的无人机总共发现了 42 名隐藏人员中的 38 名。对于预定义飞行路径的实验，平均精度为 86%，我们发现了 34 个案例中的 30 个。对于自适应抽样实验（在初始分类置信度的基础上对潜在发现进行双重检查），所有 8 个隐藏人员都被找到，导致平均精度为 100%，而分类置信度平均提高了 15%。热图像处理、分类、和动态飞行路径适应是在机上实时和飞行时计算的。我们表明，基于深度学习的人员分类不受直线飞行路径段内稀疏和容易出错的采样的影响。与以前的方法相比，这一发现可以大大缩短搜索任务并将图像复杂度降低到 1/10。我们的自适应在线采样技术的目标是尽可能可靠和快速地找到人员，这在时间关键型应用程序（例如 SAR）中至关重要。我们的无人机可以在没有稳定网络覆盖的偏远地区进行 SAR 操作，因为它只向救援队传输表明检测到的分类结果，因此可以使用间歇性最小带宽连接（例如，通过卫星）进行操作。一经收到，