5G Utility Pole Planner Using Google Street View and Mask R-CNN
This work addresses the practical challenge of efficiently planning 5G infrastructure deployment for smart city developers, though it appears incremental by adapting existing methods to a specific domain.
This paper tackles the problem of identifying suitable street lighting poles for 5G access points in smart cities by using Mask R-CNN on Google Street View images, achieving a test error rate of 32.03% and applying an immune algorithm for pole placement.
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.