论文标题
无人机中有效的资源管理以进行视觉帮助
Efficient resource management in UAVs for Visual Assistance
论文作者
论文摘要
在世界各地，使用无人驾驶汽车（无人机）在农业，军事，灾难管理和航空摄影中使用无人驾驶汽车（UAV）越来越兴趣。无人机是可扩展的，灵活的，并且在困难直接干预的各种环境中很有用。通常，由于它们在现实生活中的广泛应用中，将无人机与安装在其上的相机的使用增加了数量。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的成功。但是大多数评估模型都是在高端CPU和GPU上完成的。实时使用无人机进行视觉辅助任务的主要挑战之一是管理这些任务的内存使用和功耗，这些任务在计算上很密集，难以在无人机的低端处理器板上执行。该项目描述了一种新颖的方法，可以在实时场景中优化无人机硬件的通用图像处理任务，而不会影响飞行时间，而不会篡改这些模型的延迟和准确性。
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.
