# Automated Parking Space Detection Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2106.07228 · HTML: https://mlsanity.com/paper/2106.07228/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 1 of 1)

- [cuDNN: Efficient Primitives for Deep Learning](https://mlsanity.com/paper/1410.0759/) (2014)

## Built on by (most cited first, 6 of 6)

- [A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets](https://mlsanity.com/paper/2203.06463/) (2022)
- [Smart Parking Space Detection under Hazy conditions using Convolutional Neural Networks: A Novel Approach](https://mlsanity.com/paper/2201.05858/) (2022)
- [Towards an Error-free Deep Occupancy Detector for Smart Camera Parking System](https://mlsanity.com/paper/2208.08220/) (2022)
- [Public Parking Spot Detection And Geo-localization Using Transfer Learning](https://mlsanity.com/paper/2209.00213/) (2022)
- [Smart Camera Parking System With Auto Parking Spot Detection](https://mlsanity.com/paper/2407.05469/) (2024)
- [Spot-Wise Smart Parking: An Edge-Enabled Architecture with YOLOv11 and Digital Twin Integration](https://mlsanity.com/paper/2602.01754/) (2026)

## Abstract (arXiv)

Finding a parking space nowadays becomes an issue that is not to be neglected, it consumes time and energy. We have used computer vision techniques to infer the state of the parking lot given the data collected from the University of The Witwatersrand. This paper presents an approach for a real-time parking space classification based on Convolutional Neural Networks (CNN) using Caffe and Nvidia DiGITS framework. The training process has been done using DiGITS and the output is a caffemodel used for predictions to detect vacant and occupied parking spots. The system checks a defined area whether a parking spot (bounding boxes defined at initialization of the system) is containing a car or not (occupied or vacant). Those bounding box coordinates are saved from a frame of the video of the parking lot in a JSON format, to be later used by the system for sequential prediction on each parking spot. The system has been trained using the LeNet network with the Nesterov Accelerated Gradient as solver and the AlexNet network with the Stochastic Gradient Descent as solver. We were able to get an accuracy on the validation set of 99% for both networks. The accuracy on a foreign dataset(PKLot) returned as well 99%. Those are experimental results based on the training set shows how robust the system can be when the prediction has to take place in a different parking space.