  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.
