# Public Parking Spot Detection And Geo-localization Using Transfer Learning

arXiv: https://arxiv.org/abs/2209.00213 · HTML: https://mlsanity.com/paper/2209.00213/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 1 Sep 2022.

## Builds on (most cited first, 5 of 5)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Automated Parking Space Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/2106.07228/) (2021)

## Built on by (most cited first, 1 of 1)

- [Simulating Refractive Distortions and Weather-Induced Artifacts for Resource-Constrained Autonomous Perception](https://mlsanity.com/paper/2507.05536/) (2025)

## Abstract (arXiv)

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.