  In cities around the world, locating public parking lots with vacant parking
spots is a major problem, costing commuters time and adding to traffic
congestion. This work illustrates how a dataset of Geo-tagged images from a
mobile phone camera, can be used in navigating to the most convenient public
parking lot in Johannesburg with an available parking space, detected by a
neural network powered-public camera. The images are used to fine-tune a
Detectron2 model pre-trained on the ImageNet dataset to demonstrate detection
and segmentation of vacant parking spots, we then add the parking lot's
corresponding longitude and latitude coordinates to recommend the most
convenient parking lot to the driver based on the Haversine distance and number
of available parking spots. Using the VGG Image Annotation (VIA) we use images
from an expanding dataset of images, and annotate these with polygon outlines
of the four different types of objects of interest: cars, open parking spots,
people, and car number plates. We use the segmentation model to ensure number
plates can be occluded in production for car registration anonymity purposes.
We get an 89% and 82% intersection over union cover score on cars and parking
spaces respectively. This work has the potential to help reduce the amount of
time commuters spend searching for free public parking, hence easing traffic
congestion in and around shopping complexes and other public places, and
maximize people's utility with respect to driving on public roads.
