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arXiv · 2309.16495

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

Andre Gustavo Hochuli·Jean Paul Barddal·Gillian Cezar Palhano·Leonardo Matheus Mendes·Paulo Ricardo Lisboa de Almeida
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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

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Andre Gustavo Hochuli, Jean Paul Barddal, Gillian Cezar Palhano, Leonardo Matheus Mendes, Paulo Ricardo Lisboa de Almeida. 2023-09-28. Deep Single Models vs. Ensembles: Insights for a Fast Deployment of Parking Monitoring Systems. https://arxiv.org/abs/2309.16495

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