Robust Real time Lightweight Automatic License plate Recognition System for Iranian License Plates

Alborzi, Yusef; Mehraban, Talayeh Sarraf; Khoramdel, Javad; Ardekany, Ali Najafi

doi:10.1109/icrom48714.2019.9071863

Cited by 7 publications

(1 citation statement)

References 29 publications

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“…However, overcoming this challenge, few successful attempts have been made to develop lightweight ALPR models that can execute on edge devices with limited resources. A study by Alborzi et al 19 have proposed a lightweight ALPR model that is implementable purely on embedded devices like Raspberry Pi3. They have used a combination of MobileNet feature extractor with fewer parameters along with a Single‐shot detection model to achieve the least usage of memory for the detection stage.…”

Section: Introductionmentioning

confidence: 99%

License plate recognition using neural architecture search for edge devices

Shashirangana

Padmasiri

Meedeniya

et al. 2021

Int J of Intelligent Sys

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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.

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Section: Introductionmentioning

confidence: 99%