Shizhen Huang1,2, Mengru Lin1,2, Fan Yu3, Ruiqi Chen This email address is being protected from spambots. You need JavaScript enabled to view it.1,2,3, Lei Zhang1, and Yanxiang Zhu3

1College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
2Fujian Key Lab of Microelectronics & Integrated Circuits, Fuzhou University, Fuzhou 350002, China
3Verimake Research, Nanjing Qujike Info-tech Co., Ltd., Nanjing 210088, China


 

Received: December 18, 2020
Accepted: May 24, 2021
Publication Date: July 5, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202202_25(1).0001  


ABSTRACT


Automatic License Plate Recognition (ALPR) systems have become popular application areas of the Internet of Things (IoT). A typical ALPR system always needs powerful processors such as Cortex-A7. However, most known system for Standard Definition (SD) are not suitable for real-time High Definition (HD) image processing and low power consuming requirement in IoT. A HD ALPR accelerator for the IoT endpoint System on Chip (SoC) is proposed in this paper to meet the needs of computations. Based on the programming flexibility of IoT endpoint SoC, it can switch between HD and SD resolutions, which can avoid the specific resolution switching algorithm. A Field Programmable Gate Array (FPGA) chip is transplanted the Cortex-M0 as the IoT endpoint SoC, through the design of ALPR accelerator and Cortex-M0, data communication is achieved by First-In, First-Out (FIFO) with AMBA High-performance Bus (AHB) interface. Heterogeneous implementation of ALPR system has shown that this HD ALPR algorithm can recognize a license plate in 12.5ms, with a success rate of 95.5%. The system utilizes 41,763 Look-Up-Tables (LUTs) without special FPGA IP core. The comparison shows that the system proposed in this paper makes performance of the SoC based on the Cortex-M0 kernel was two times higher than the Cortex-A72 SoC and 39% of the power consumption of Zynq-7000 that is typicalheterogeneous ALPR platform.


Keywords: Automatic License Plate Recognition (ALPR); Internet of Things (IoT); FPGA; SoC; Cortex-M0


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