# Deep Single Models vs. Ensembles: Insights for a Fast Deployment of Parking Monitoring Systems

arXiv: https://arxiv.org/abs/2309.16495 · HTML: https://mlsanity.com/paper/2309.16495/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 28 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets](https://mlsanity.com/paper/2203.06463/) (2022)
- [Evaluation of Different Annotation Strategies for Deployment of Parking Spaces Classification Systems](https://mlsanity.com/paper/2207.11372/) (2022)
- [Vehicle Occurrence-based Parking Space Detection](https://mlsanity.com/paper/2306.09940/) (2023)
- [Automatic Vision-Based Parking Slot Detection and Occupancy Classification](https://mlsanity.com/paper/2308.08192/) (2023)

## Built on by (most cited first, 4 of 4)

- [Optimizing Parking Space Classification: Distilling Ensembles into Lightweight Classifiers](https://mlsanity.com/paper/2410.14705/) (2024)
- [Using Deep Neural Networks to Quantify Parking Dwell Time](https://mlsanity.com/paper/2411.00158/) (2024)
- [Toward Parking Spot Occupancy Recognition: A Self-Supervised Approach](https://mlsanity.com/paper/2606.20886/) (2026)
- [An Ensemble-Based Self-Taught Learning Approach for Parking Space Classification Under Limited Data](https://mlsanity.com/paper/2609.03258/) (2026)

## Abstract (arXiv)

Searching for available parking spots in high-density urban centers is a stressful task for drivers that can be mitigated by systems that know in advance the nearest parking space available. To this end, image-based systems offer cost advantages over other sensor-based alternatives (e.g., ultrasonic sensors), requiring less physical infrastructure for installation and maintenance. Despite recent deep learning advances, deploying intelligent parking monitoring is still a challenge since most approaches involve collecting and labeling large amounts of data, which is laborious and time-consuming. Our study aims to uncover the challenges in creating a global framework, trained using publicly available labeled parking lot images, that performs accurately across diverse scenarios, enabling the parking space monitoring as a ready-to-use system to deploy in a new environment. Through exhaustive experiments involving different datasets and deep learning architectures, including fusion strategies and ensemble methods, we found that models trained on diverse datasets can achieve 95% accuracy without the burden of data annotation and model training on the target parking lot