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

TL;DR

This paper investigates the use of deep learning models and ensemble strategies for parking space monitoring, demonstrating that models trained on diverse datasets can achieve high accuracy without extensive data collection or retraining.

Contribution

It provides insights into deploying a robust, ready-to-use parking monitoring system using publicly available data and ensemble methods, reducing the need for laborious data annotation.

Findings

Models trained on diverse datasets reach 95% accuracy.

Ensemble strategies improve detection robustness.

Publicly available datasets suffice for accurate parking monitoring.

Abstract

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