HMD基于视觉的远程操作UGV和UAV，用于使用深度学习的敌对环境
HMD Vision-based Teleoperating UGV and UAV for Hostile Environment using Deep Learning
摘要：
随着社会上流氓分子的死灰复燃，维持一支强有力的反恐工作队的必要性已变得势在必行。一支装备精良的战斗部队保证了公民的安全和保障以及主权国家的完整。在本文中，我们提出了一种新的遥操作机器人，它可以在战斗，救援和侦察任务中发挥重要作用，大大减少了人类士兵在这种恶劣的环境中的损失。拟议的机器人解决方案包括一个配备IP摄像机视觉系统的无人驾驶地面车辆，该系统将实时视频数据广播到远程云服务器。随着机器学习算法在计算机视觉领域的进步，我们结合了最先进的深度卷积神经网络来识别和预测具有恶意意图的个人。在云服务器中通过训练好的网络对视频流的每一帧进行分类。网络的预测输出覆盖在具有特定颜色标记和预测百分比的视频流上。最后，数据被调整大小为半并排格式，并流传输到由人类控制器佩戴的头戴式显示器，这有助于场景的第一人称视图。地面车辆还与无人驾驶飞行器相结合，进行空中监视。所提出的方案是一个辅助系统，最终的决定显然在于人类处理器。
英文摘要：
The necessity of maintaining a robust antiterrorist task force has become imperative in recent times with resurgence of rogue element in the society. A well equipped combat force warrants the safety and security of citizens and the integrity of the sovereign state. In this paper we propose a novel teleoperating robot which can play a major role in combat, rescue and reconnaissance missions by substantially reducing loss of human soldiers in such hostile environments. The proposed robotic solution consists of an unmanned ground vehicle equipped with an IP camera visual system broadcasting real-time video data to a remote cloud server. With the advancement in machine learning algorithms in the field of computer vision, we incorporate state of the art deep convolutional neural networks to identify and predict individuals with malevolent intent. The classification is performed on every frame of the video stream by the trained network in the cloud server. The predicted output of the network is overlaid on the video stream with specific colour marks and prediction percentage. Finally the data is resized into half-side by side format and streamed to the head mount display worn by the human controller which facilitates first person view of the scenario. The ground vehicle is also coupled with an unmanned aerial vehicle for aerial surveillance. The proposed scheme is an assistive system and the final decision evidently lies with the human handler.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
