Abstract
In this paper, we present a subsystem, using Unmanned Aerial Vehicles (UAV), for search and rescue missions, focusing on people detection, face recognition and tracking of identified individuals. The proposed solution integrates a UAV with ROS2 framework, that utilizes multiple convolutional neural networks (CNN) for search missions. System identification and PD controller deployment are performed for autonomous UAV navigation. The ROS2 environment utilizes the YOLOv11 and YOLOv11-pose CNNs for tracking purposes, and the dlib library CNN for face recognition. The system detects a specific individual, performs face recognition and starts tracking. If the individual is not yet known, the UAV operator can manually locate the person, save their facial image and immediately initiate the tracking process. The tracking process relies on specific keypoints identified on the human body using the YOLOv11-pose CNN model. These keypoints are used to track a specific individual and maintain a safe distance. To enhance accurate tracking, system identification is performed, based on measurement data from the UAVs IMU. The identified system parameters are used to design PD controllers that utilize YOLOv11-pose to estimate the distance between the UAVs camera and the identified individual. The initial experiments, conducted on 14 known individuals, demonstrated that the proposed subsystem can be successfully used in real time. The next step involves implementing the system on a large experimental UAV for field use and integrating autonomous navigation with GPS-guided control for rescue operations planning.
Abstract (translated)
在这篇论文中，我们提出了一种使用无人驾驶航空器（UAV）的子系统，用于搜索和救援任务，重点在于人员检测、面部识别以及对已识别个人进行追踪。该解决方案将UAV与ROS2框架相结合，并利用多个卷积神经网络（CNN）执行搜索任务。为了实现自主飞行导航，进行了系统辨识并部署了PD控制器。在ROS2环境中，使用YOLOv11和YOLOv11-pose CNN来执行追踪任务，而dlib库的CNN用于面部识别。该系统能够检测特定个体、进行面部识别，并开始跟踪。如果尚未认识该个体，UAV操作员可以手动定位此人，保存其面部图像并立即启动追踪过程。追踪过程中，使用YOLOv11-pose CNN模型在人体上确定的关键点来追踪特定个人并保持安全距离。为了提高精确的追踪效果，基于UAV惯性测量单元（IMU）提供的测量数据进行了系统辨识，并利用该参数设计了PD控制器以通过YOLOv11-pose估算无人机摄像头与识别个体之间的距离。在初始实验中，我们对14名已知人员进行了测试，证明所提出的子系统可以实现实时应用。下一步计划是将该系统集成到一个大型试验用UAV上进行实地使用，并结合GPS引导控制以实现救援操作的规划。
URL
https://arxiv.org/abs/2507.18160
