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
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