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

翻译：在城市高密度中心区域寻找可用停车位对驾驶员而言是一项压力巨大的任务，而能够提前预知最近可用停车位的系统可缓解这一问题。为此，基于图像的系统相比其他基于传感器的替代方案（例如超声波传感器）具有成本优势，所需安装和维护的物理基础设施更少。尽管深度学习近期取得了进展，但部署智能停车监控仍具挑战性，因为大多数方法涉及收集和标注大量数据，既费时又费力。本研究旨在揭示构建一个全局框架的难点：该框架利用公开可用的标注停车位图像进行训练，能在多样场景下准确运行，从而将停车位监控作为可直接部署到新环境的即用系统。通过涉及不同数据集和深度学习架构（包括融合策略与集成方法）的全面实验，我们发现：在多样化数据集上训练的模型无需在目标停车场进行数据标注与模型训练，即可达到95%的准确率。