License plate recognition using neural architecture search for edge devices

Shashirangana, Jithmi, Padmasiri, Heshan, Meedeniya, Dulani, Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346, Nayak, Soumya R., Nayak, Janmenjoy, Vimal, Shanmuganthan and Kadry, Seifidine 2022. License plate recognition using neural architecture search for edge devices. International Journal of Intelligent Systems 37 (12) , pp. 10211-10248. 10.1002/int.22471

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Abstract

The mutually beneficial blend of artificial intelligence with internet of things has been enabling many industries to develop smart information processing solutions. The implementation of technology enhanced industrial intelligence systems is challenging with the environmental conditions, resource constraints and safety concerns. With the era of smart homes and cities, domains like automated license plate recognition (ALPR) are exploring automate tasks such as traffic management and fraud detection. This paper proposes an optimized decision support solution for ALPR that works purely on edge devices at night-time. Although ALPR is a frequently addressed research problem in the domain of intelligent systems, still they are generally computationally intensive and unable to run on edge devices with limited resources. Therefore, as a novel approach, we consider the complex aspects related to deploying lightweight yet efficient and fast ALPR models on embedded devices. The usability of the proposed models is assessed in real-world with a proof-of-concept hardware design and achieved competitive results to the state-of-the-art ALPR solutions that run on server-grade hardware with intensive resources.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Wiley
ISSN:	0884-8173
Date of First Compliant Deposit:	28 January 2022
Date of Acceptance:	4 May 2021
Last Modified:	12 Jan 2023 12:18
URI:	https://orca.cardiff.ac.uk/id/eprint/147060

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