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Automated license plate recognition for resource-constrained environments

Padmasiri, Heshan, Shashirangana, Jithmi, Meedeniya, Dulani, Rana, Omer ORCID: and Perera, Charith ORCID: 2022. Automated license plate recognition for resource-constrained environments. Sensors 22 (4) , 1434. 10.3390/s22041434

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The incorporation of deep-learning techniques in embedded systems has enhanced the capabilities of edge computing to a great extent. However, most of these solutions rely on high-end hardware and often require a high processing capacity, which cannot be achieved with resource-constrained edge computing. This study presents a novel approach and a proof of concept for a hardware-efficient automated license plate recognition system for a constrained environment with limited resources. The proposed solution is purely implemented for low-resource edge devices and performed well for extreme illumination changes such as day and nighttime. The generalisability of the proposed models has been achieved using a novel set of neural networks for different hardware configurations based on the computational capabilities and low cost. The accuracy, energy efficiency, communication, and computational latency of the proposed models are validated using different license plate datasets in the daytime and nighttime and in real time. Meanwhile, the results obtained from the proposed study have shown competitive performance to the state-of-the-art server-grade hardware solutions as well.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Additional Information: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// 4.0/).
Publisher: MDPI
ISSN: 1424-8220
Date of First Compliant Deposit: 28 February 2022
Date of Acceptance: 11 February 2022
Last Modified: 10 Nov 2022 10:39

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