Automated Vehicle Parking Occupancy Detection in Real-Time

Padmasiri, Heshan; Madurawe, Ranika N.; Abeysinghe, Chamath; Meedeniya, Dulani

doi:10.1109/mercon50084.2020.9185199

Cited by 17 publications

(13 citation statements)

References 18 publications

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“…First, a latency table is created for the execution of each operation on the target hardware. Then, we use the latency lookup table and calculate the latency of layer i using value p i as shown in Equation (5).…”

Section: Performance Estimation Strategymentioning

confidence: 99%

“…The emergence of edge computing has unveiled an exceptional proliferation of computerintensive applications for smart cities [1,2] and smart homes [3] for different domains such as security [4], city parking [5] and traffic management [6]. Most of these modern systems involve capabilities beyond traditional computing by embedding edge intelligence to enable self-learning solutions including machine learning and deep learning [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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Automated License Plate Recognition for Resource-Constrained Environments

Padmasiri

Shashirangana

Meedeniya

et al. 2022

Sensors

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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.

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Section: Performance Estimation Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated License Plate Recognition for Resource-Constrained Environments

Padmasiri

Shashirangana

Meedeniya

et al. 2022

Sensors

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“…Métodos para a segmentação automática de vagas de estacionamento utilizando técnicas clássicas de processamento de imagens, como transformações de perspectivas, binarizações e detectores de borda, são propostos em [Bohush et al 2018, Zhang et al 2019, Zhang and Du 2020. Extratores de características são usados em [Vítek and Melničuk 2018] para detectar se blocos da imagem pertencem ou não a carros, e métodos baseados em redes neurais convolucionais são apresentados em [Agrawal and Urolagin 2020, Padmasiri et al 2020, Hurst-Tarrab et al 2020.…”

Section: Estado Da Arteunclassified

“…Apesar de existirem métodos tratando da segmentação automática de vagas, os trabalhos são escassos e em sua grande maioria apresentam dados insuficientes sobre os métodos, experimentos e principalmente resultados. Dos trabalhos citados, apenas [Padmasiri et al 2020, Hurst-Tarrab et al 2020] apresentam resultados quantitativos.…”

Section: Estado Da Arteunclassified

“…No contexto dos transportes, otimizações nos sistemas de temporização de semáforos [Li et al 2017, Djahel et al 2013, otimização do tráfego para serviços de emergência [Djahel et al 2013], predição das condições de tráfego [Nagy and Simon 2018] e a gerência de vagas de estacionamento [de Almeida et al 2015, Amato et al 2017, Padmasiri et al 2020] podem levar a diversos benefícios, como a redução de congestionamentos, redução do consumo de energia (e.g., combustíveis fósseis ou eletricidade), menor poluição, e um aumento na qualidade de vida dos habitantes.…”

Section: Introductionunclassified

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Desafios na Gerência de Estacionamentos por Imagem

Almeida¹,

Almeida²

2021

Anais Do XLVIII Seminário Integrado De Software E Hardware (SEMISH 2021)

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Com o aumento na população urbana, as cidades tem grande necessidade de utilizar métodos apoiados por técnicas de Inteligência Artificial para melhorar a mobilidade urbana em trânsitos cada vez mais congestionados pelo crescente número de veículos em circulação. Estudos demonstram que o problema de congestionamento de trânsito é agravado em até 30% por veículos procurando vagas de estacionamento. Neste artigo sumarizamos problemas em aberto na gerência de vagas de estacionamento por imagem e listamos nossos esforços de pesquisa combinando técnicas de aprendizado de máquina, edge computing e processamento de imagens.

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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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Automated Vehicle Parking Occupancy Detection in Real-Time

Cited by 17 publications

References 18 publications

Automated License Plate Recognition for Resource-Constrained Environments

Automated License Plate Recognition for Resource-Constrained Environments

Desafios na Gerência de Estacionamentos por Imagem

License plate recognition using neural architecture search for edge devices

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