# Optimizing Parking Space Classification: Distilling Ensembles into Lightweight Classifiers

arXiv: https://arxiv.org/abs/2410.14705 · HTML: https://mlsanity.com/paper/2410.14705/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Knowledge Distillation in Deep Learning and its Applications](https://mlsanity.com/paper/2007.09029/) (2020)
- [A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets](https://mlsanity.com/paper/2203.06463/) (2022)
- [Parking Stall Vacancy Indicator System Based on Deep Convolutional Neural Networks](https://mlsanity.com/paper/1606.09367/) (2016)
- [Evaluation of Different Annotation Strategies for Deployment of Parking Spaces Classification Systems](https://mlsanity.com/paper/2207.11372/) (2022)
- [Deep Single Models vs. Ensembles: Insights for a Fast Deployment of Parking Monitoring Systems](https://mlsanity.com/paper/2309.16495/) (2023)
- [Automatic Vision-Based Parking Slot Detection and Occupancy Classification](https://mlsanity.com/paper/2308.08192/) (2023)

## Built on by (most cited first, 2 of 2)

- [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)

When deploying large-scale machine learning models for smart city applications, such as image-based parking lot monitoring, data often must be sent to a central server to perform classification tasks. This is challenging for the city's infrastructure, where image-based applications require transmitting large volumes of data, necessitating complex network and hardware infrastructures to process the data. To address this issue in image-based parking space classification, we propose creating a robust ensemble of classifiers to serve as Teacher models. These Teacher models are distilled into lightweight and specialized Student models that can be deployed directly on edge devices. The knowledge is distilled to the Student models through pseudo-labeled samples generated by the Teacher model, which are utilized to fine-tune the Student models on the target scenario. Our results show that the Student models, with 26 times fewer parameters than the Teacher models, achieved an average accuracy of 96.6% on the target test datasets, surpassing the Teacher models, which attained an average accuracy of 95.3%.