论文标题

实时视觉任务的高效基于无人机的人工智能框架

An Efficient UAV-based Artificial Intelligence Framework for Real-Time Visual Tasks

论文作者

Togootogtokh, Enkhtogtokh, Micheloni, Christian, Foresti, Gian Luca, Martinel, Niki

论文摘要

配备有艺术人工智能状态(AI)技术的现代无人飞机正在开放大量新颖而有趣的应用。尽管该领域从最近的AI突破中受到了很大的影响,但大多数提供的解决方案要么完全依赖商业软件,要么提供薄弱的集成接口,从而剥夺了其他技术的开发。这使我们为UAV-AI联合技术提出了一个新颖有效的框架。智能无人机系统会遇到无人控制的复杂挑战。这些复杂的挑战之一是能够在实时用例中执行计算机视觉任务。在本文中,我们关注这一挑战,并引入多层AI(MLAI)框架,以便于易于集成基于视觉的AI应用程序。为了展示其功能和优势,我们实施并评估了不同的基于现代视觉的深度学习模型,以进行对象检测,目标跟踪和目标移交。

Modern Unmanned Aerial Vehicles equipped with state of the art artificial intelligence (AI) technologies are opening to a wide plethora of novel and interesting applications. While this field received a strong impact from the recent AI breakthroughs, most of the provided solutions either entirely rely on commercial software or provide a weak integration interface which denies the development of additional techniques. This leads us to propose a novel and efficient framework for the UAV-AI joint technology. Intelligent UAV systems encounter complex challenges to be tackled without human control. One of these complex challenges is to be able to carry out computer vision tasks in real-time use cases. In this paper we focus on this challenge and introduce a multi-layer AI (MLAI) framework to allow easy integration of ad-hoc visual-based AI applications. To show its features and its advantages, we implemented and evaluated different modern visual-based deep learning models for object detection, target tracking and target handover.

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