论文标题
使用Google Street View和Mask R-CNN的5G公用电台计划器
5G Utility Pole Planner Using Google Street View and Mask R-CNN
论文作者
论文摘要
随着第五代（5G）蜂窝网络技术的进步，许多研究和工作已经进行了如何为智能城市建立5G网络。在先前的研究中，街道照明杆和智能灯杆能够成为5G接入点。为了确定要点的位置，本文讨论了一种基于面膜R-CNN识别极点的新方法，该方法通过使其采用递归贝叶斯过滤并执行提案的传播和重复使用来扩展快速的R-CNN。该数据集包含来自Google Map中的3,000张高分辨率图像。为了更快地进行培训，我们使用了卷积操作的非常有效的GPU实施。我们达到的火车错误率为7.86％，测试错误率为32.03％。最后，我们使用免疫算法在智能城市中设置5G杆。
With the advances of fifth-generation (5G) cellular networks technology, many studies and work have been carried out on how to build 5G networks for smart cities. In the previous research, street lighting poles and smart light poles are capable of being a 5G access point. In order to determine the position of the points, this paper discusses a new way to identify poles based on Mask R-CNN, which extends Fast R-CNNs by making it employ recursive Bayesian filtering and perform proposal propagation and reuse. The dataset contains 3,000 high-resolution images from google map. To make training faster, we used a very efficient GPU implementation of the convolution operation. We achieved a train error rate of 7.86% and a test error rate of 32.03%. At last, we used the immune algorithm to set 5G poles in the smart cities.
