VOMTC: 面向毫米波与太赫兹通信的视觉对象
VOMTC: Vision Objects for Millimeter and Terahertz Communications
AI总结：
本文提出面向毫米波与太赫兹通信的大规模视觉数据集VOMTC，含20232组基站采集的RGB-深度图像对及标注，实验验证基于该数据集训练的目标检测器可提升波束成形性能。
摘要：
传感与计算机视觉（CV）技术的最新进展，为基于深度学习（DL）的CV技术在6G无线通信领域的应用打开了大门。要成功应用这一新兴技术，拥有一套适配无线应用的高质量视觉数据集（例如包含笔记本电脑、手机等无线设备的RGB图像）至关重要。本文的目标之一是提出一个名为Vision Objects for Millimeter and Terahertz Communications（VOMTC，面向毫米波与太赫兹通信的视觉对象）的大规模视觉数据集。VOMTC数据集包含20232组由基站（BS）搭载的摄像头采集的RGB与深度图像对，每组图像都标注了三类代表性对象类别（人、手机、笔记本电脑）以及对象的边界框。通过基于VOMTC数据集的实验研究，我们证明利用在VOMTC上训练的目标检测器的波束成形技术，性能优于传统波束成形技术。
英文摘要：
Recent advances in sensing and computer vision (CV) technologies have opened the door for the application of deep learning (DL)-based CV technologies in the realm of 6G wireless communications. For the successful application of this emerging technology, it is crucial to have a qualified vision dataset tailored for wireless applications (e.g., RGB images containing wireless devices such as laptops and cell phones). An aim of this paper is to propose a large-scale vision dataset referred to as Vision Objects for Millimeter and Terahertz Communications (VOMTC). The VOMTC dataset consists of 20,232 pairs of RGB and depth images obtained from a camera attached to the base station (BS), with each pair labeled with three representative object categories (person, cell phone, and laptop) and bounding boxes of the objects. Through experimental studies of the VOMTC datasets, we show that the beamforming technique exploiting the VOMTC-trained object detector outperforms conventional beamforming techniques.
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。
