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Smart connected parking lots based on secured multimedia loT devices

机译:基于安全的多媒体批量设备的智能连接的停车场

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摘要

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 l(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.
机译:在本文中,我们展示了一个智能连接的停车场解决方案,可以自动计算和通知司机关于主要城市的空停车位。由于其名称意味着,所提出的智能物联网系统有两个操作阶段:(i)通过轻量级MQTT机制连续计数被监控的远交换停车场中的空点,并通过轻量级MQTT机制进行瞬时驱动器通知。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,例如接地传感器)。为了验证我们解决方案的正确操作,我们已经实施了一定的小规模版本,并在考虑不同的经典和轻量级深度学习技术时评估其性能(MobileNetv2,Resnet-50,Yolov3,SSD-MobileNetv2,Tiny-Yolo,Screezedet和L(1)-norm修剪的挤压仪。实验确认了我们系统的适当运行,效率,易于部署和易于延伸。他们还确认,轻量级的深度学习解决方案更适合小型资源受限嵌入式系统。它们在推理时间,大小,资源消耗方面更有效,并产生接近经典解决方案的准确性。

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