论文标题
基于视觉的系统，用于实时检测和无人机的关注
Vision-based system for a real-time detection and following of UAV
论文作者
论文摘要
在本文中，提出了一个基于视觉的检测系统，带有其他无人机（跟随器）的无人机（UAV）的运动跟踪和跟随。为了检测空降无人机，我们应用了在收集和处理的10,000张图像的数据集中训练的卷积神经网络Yolo。训练有素的网络能够检测室内，室外和仿真环境中的各种多旋风无人机。此外，通过Kalman滤光片改善了检测结果，该滤波器可确保有关目标无人机的位置和速度的稳定信息。在视野（FOV）中保留目标无人机，并在所需距离处通过基于视觉伺服策略的简单非线性控制器来完成。所提出的系统在神经计算棒2上实现了实时性能，每秒20帧（FPS）的速度可检测无人机。在凉亭仿真实验中确认了基于开发的视觉系统的理由和效率，其中目标无人机以第8号形状执行3D轨迹。
In this paper a vision-based system for detection, motion tracking and following of Unmanned Aerial Vehicle (UAV) with other UAV (follower) is presented. For detection of an airborne UAV we apply a convolutional neural network YOLO trained on a collected and processed dataset of 10,000 images. The trained network is capable of detecting various multirotor UAVs in indoor, outdoor and simulation environments. Furthermore, detection results are improved with Kalman filter which ensures steady and reliable information about position and velocity of a target UAV. Preserving the target UAV in the field of view (FOV) and at required distance is accomplished by a simple nonlinear controller based on visual servoing strategy. The proposed system achieves a real-time performance on Neural Compute Stick 2 with a speed of 20 frames per second (FPS) for the detection of an UAV. Justification and efficiency of the developed vision-based system are confirmed in Gazebo simulation experiment where the target UAV is executing a 3D trajectory in a shape of number eight.
