论文标题
使用无人驾驶汽车和卷积姿势机进行连接的人工智能测试床
Testbed for Connected Artificial Intelligence using Unmanned Aerial Vehicles and Convolutional Pose Machines
论文作者
论文摘要
近年来，无人驾驶飞机（UAV）在大量应用中非常受欢迎，尤其是由机上摄像机和嵌入式系统启用具有计算机视觉功能的无人机。其中许多使用集成相机收集的数据应用对象检测。但是，实时对象检测的几种应用取决于卷积神经网络（CNN），这些神经网络（CNN）在计算上昂贵，并且在无人机平台上处理CNN的加工CNN具有挑战性（由于电池寿命有限和处理能力有限）。为了了解这些问题的效果，在本文中，我们评估了处理UAV中的整个数据与边缘计算设备中的整个数据的约束和好处。我们将卷积姿势机（CPMS）应用于铰接姿势估计的任务。我们使用这些信息来检测人类手势，这些手势被用作输入以发送命令以控制无人机。使用真实无人机的实验结果表明，边缘处理效率更高，更快（W.R.T电池消耗以及识别人姿势和给出无人机的命令的延迟）比无人机处理更适合基于CNNS的应用。
Unmanned Aerial Vehicles (UAVs) became very popular in a vast number of applications in recent years, especially drones with computer vision functions enabled by on-board cameras and embedded systems. Many of them apply object detection using data collected by the integrated camera. However, several applications of real-time object detection rely on Convolutional Neural Networks (CNNs) which are computationally expensive and processing CNNs on a UAV platform is challenging (due to its limited battery life and limited processing power). To understand the effects of these issues, in this paper we evaluate the constraints and benefits of processing the whole data in the UAV versus in an edge computing device. We apply Convolutional Pose Machines (CPMs) known as OpenPose for the task of articulated pose estimation. We used this information to detect human gestures that are used as input to send commands to control the UAV. The experimental results using a real UAV indicate that the edge processing is more efficient and faster (w.r.t battery consumption and the delay in recognizing the human pose and the command given to the drone) than UAV processing and then could be more suitable for CNNs based applications.
