用于视觉辅助的无人机中的有效资源管理
Efficient resource management in UAVs for Visual Assistance
摘要：
世界各地对无人机（UAV）用于农业、军事、灾害管理和航空摄影的兴趣越来越大。无人机是可扩展的，灵活的，并在各种环境中，直接人为干预是困难的。一般来说，由于其在现实生活场景中的广泛应用，安装有相机的UAV的使用数量已经增加。随着计算机视觉中深度学习模型的出现，许多模型在视觉任务中取得了巨大成功。但是大多数的评估模型都是在高端的CPU和GPU上完成的。使用UAV进行实时的视觉辅助任务的主要挑战之一是管理这些任务的存储器使用和功耗，这些任务是计算密集型的并且难以在UAV的低端处理器板上执行。该项目描述了一种新的方法来优化一般的图像处理任务，如对象跟踪和目标检测的无人机硬件在实时的场景，而不影响飞行时间和不篡改这些模型的延迟和准确性。
英文摘要：
There is an increased interest in the use of Unmanned Aerial Vehicles (UAVs) for agriculture, military, disaster management and aerial photography around the world. UAVs are scalable, flexible and are useful in various environments where direct human intervention is difficult. In general, the use of UAVs with cameras mounted to them has increased in number due to their wide range of applications in real life scenarios. With the advent of deep learning models in computer vision many models have shown great success in visual tasks. But most of evaluation models are done on high end CPUs and GPUs. One of major challenges in using UAVs for Visual Assistance tasks in real time is managing the memory usage and power consumption of the these tasks which are computationally intensive and are difficult to be performed on low end processor board of the UAV. This projects describes a novel method to optimize the general image processing tasks like object tracking and object detection for UAV hardware in real time scenarios without affecting the flight time and not tampering the latency and accuracy of these models.
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。
