Parking Occupancy Classification: Deep learning model compression for edge device classification

Abstract

Urban areas face severe traffic congestion due to poorly managed parking systems. Advanced parking management, like automated and smart parking guidance systems, offers a feasible solution requiring real-tim occupancy data. Traditional sensor-based methods are costly and inefficient for large scale parking, whereas video-based sensing is more effective. Deep learning methods improve accuracy but have high computational costs, affecting real-time performance. Central servers or cloud platforms are often used but can be impractical due to high resource demands. Instead, utilising edge devices with model compression techniques—such as quantisation and knowledge distillation enhances efficiency. This study aims to boost the inference speed of parking classification algorithms by developing a custom model called QCustom based on the MobileNet Depthwise Separable Convolution blocks and using compression techniques to reduce the inference time further. Contributions include developing an edge-based real-time occupancy system, setting performance benchmarks, optimising models for edge devices, and testing on a prototype parking lot. The goal is efficient and accurate parking management for smart cities. Results of the paper include accuracy of 98.8% on the CNRPark-EXT dataset, real world implementation accuracy of 97.44%, and an inference speed for one parking slot of 0.746ms on the Raspberry Pi 5.