构建毫米波太赫兹通信视觉数据集，提升波束成形性能
VOMTC: Vision Objects for Millimeter and Terahertz Communications
- 基于基站摄像头采集2万+对彩色与深度图像
- 用该数据集训练的检测器使波束成形性能更优
- 适合6G无线感知与智能波束成形研究者
近年来，传感与计算机视觉技术的进步为深度学习在6G无线通信中的应用打开了新路径。要实现这一新兴技术的成功落地，关键在于构建面向无线场景的高质量视觉数据集（如包含笔记本、手机等设备的RGB图像）。本文提出一个大规模视觉数据集——毫米波与太赫兹通信视觉对象数据集（VOMTC），包含20,232对由基站摄像头获取的彩色与深度图像，每对图像均标注了人、手机、笔记本三类目标及其边界框。通过在VOMTC数据集上的实验研究，验证了基于该数据集训练的物体检测器所驱动的波束成形技术，优于传统波束成形方法。
原文摘要 · Abstract (English)
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.
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