Drowning is an omnipresent risk associated with any activity on or in the water, and rescuing a drowning person is particularly challenging because of the time pressure, making a short response time important. Further complicating water rescue are unsupervised and extensive swimming areas, precise localization of the target, and the transport of rescue personnel. Technical innovations can provide a remedy: We propose an Unmanned Aircraft System (UAS), also known as a drone-in-a-box system, consisting of a fleet of Unmanned Aerial Vehicles (UAVs) allocated to purpose-built hangars near swimming areas. In an emergency, the UAS can be deployed in addition to Standard Rescue Operation (SRO) equipment to locate the distressed person early by performing a fully automated Search and Rescue (S&R) operation and dropping a flotation device. In this paper, we address automatically locating distressed swimmers using the image-based object detection architecture You Only Look Once (YOLO). We present a dataset created for this application and outline the training process. We evaluate the performance of YOLO versions 3, 5, and 8 and architecture sizes (nano, extra-large) using Mean Average Precision (mAP) metrics [email protected] and [email protected]:.95. Furthermore, we present two Discrete-Event Simulation (DES) approaches to simulate response times of SRO and UAS-based water rescue. This enables estimation of time savings relative to SRO when selecting the UAS configuration (type, number, and location of UAVs and hangars). Computational experiments for a test area in the Lusatian Lake District, Germany, show that UAS assistance shortens response time. Even a small UAS with two hangars, each containing one UAV, reduces response time by a factor of five compared to SRO.

翻译：溺水是与水上或水中活动相关的普遍风险，由于时间紧迫，救援溺水者尤为困难，因此快速响应时间至关重要。无人看管且范围广阔的游泳区域、目标的精确定位以及救援人员的运输进一步增加了水上救援的复杂性。技术创新可提供解决方案：我们提出一种无人机系统（UAS），也称为机箱一体化系统，由部署在游泳区附近专用机库中的多架无人机（UAV）组成。在紧急情况下，该无人机系统可作为标准救援操作（SRO）设备的补充，通过执行全自动搜索与救援（S&R）操作，早期定位遇险人员并投放浮力装置。本文重点研究利用基于图像的物体检测架构You Only Look Once（YOLO）自动定位遇险游泳者。我们针对此应用创建了一个数据集，并概述了训练过程。使用平均精度（mAP）指标[email protected]和[email protected]:.95评估YOLO版本3、5和8以及不同架构规模（nano、extra-large）的性能。此外，我们提出了两种离散事件仿真（DES）方法，用于模拟基于SRO和UAS的水上救援响应时间。这使得能够在选择UAS配置（无人机和机库的类型、数量及位置）时，估算相对于SRO的时间节省。针对德国卢萨蒂亚湖区测试区域的计算实验表明，UAS辅助可缩短响应时间。即使是一个配备两个机库（各含一架无人机）的小型UAS，其响应时间也比SRO缩短了五倍。