In this paper, we present a smart connected parking lots solution to automatically count and notify drivers about empty parking spots in major cities. As its name implies, the proposed smart IoT system has two operating phases: (i) continuous counting of empty spots in the monitored far-apart parking lots, and (ii) instantaneous driver notification through a lightweight MQTT mechanism. This notification system relies only on information collected from the pre-installed multimedia devices (no other apparatus installation or maintenance such as ground sensors is required). To validate the proper operation of our solution, we have implemented a small-scale version of it and assessed its performance while considering different classical and lightweight deep learning techniques (MobileNetV2, ResNet-50, YOLOv3, SSD-MobileNetV2, Tiny-YOLO, SqueezeDet, and SqueezeDet pruned with \(\ell _1\)-norm). The experiments have confirmed the proper operation, efficiency, ease of deployment, and ease of extension of our system. They also confirmed that lightweight deep learning solutions are more adequate for small-sized resource-constrained embedded systems. They are more efficient in terms of inference time, size, resource consumption, and yield an accuracy that is close to that of classical solutions.

在本文中，我们提出了一种智能联网停车场解决方案，可自动计算并通知驾驶员主要城市的空停车位。顾名思义，拟议的智能物联网系统具有两个操作阶段：（i）连续计数受监控的远处停车场中的空点，以及（ii）通过轻量级的MQTT机制即时通知驾驶员。该通知系统仅依赖于从预安装的多媒体设备收集的信息（不需要其他设备安装或维护，例如地面传感器）。为了验证我们的解决方案是否正常运行，我们在考虑了不同的经典和轻量级深度学习技术（MobileNetV2，ResNet-50，YOLOv3，SSD-MobileNetV2，Tiny-YOLO，SqueezeDet）的同时，实现了该解决方案的小型版本并评估了其性能。 ，\（\ ell _1 \） - norm）。实验已经确认了正确的操作，效率，易于部署和易于扩展我们的系统。他们还确认，轻量级深度学习解决方案更适合于资源受限的小型嵌入式系统。它们在推断时间，大小，资源消耗方面更有效，并且产生的精度接近经典解决方案。