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.
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CNN: Convolutional Neural Network.
The gradient becomes very small, which prevents the weights from changing and slows down the learning process.
MQTT: Message Queuing Telemetry Transport.
Mosquitto implements the MQTT protocol versions 5.0, 3.1.1, and 3.1.
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The authors would like to thank Mohammed Habib Allah Kechout and Sebti Tamraoui, from the Higher National School of Computer Science (Algiers), for their precious help to this work.
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Merzoug, M.A., Mostefaoui, A., Gianini, G. et al. Smart connected parking lots based on secured multimedia IoT devices. Computing (2021). https://doi.org/10.1007/s00607-021-00921-1
- In-city parking
- Parking spot availability
- Deep learning
- Connected parking lots
- Internet of things
Mathematics Subject Classification
- 68T45: Machine vision and scene understanding
- 68U10: Image processing