An Efficient Traffic Sign Recognition Approach Using a Novel Deep Neural Network Selection Architecture

Saha, Sourajit; Islam, Md. Saiful; Khaled, Md. Asif Bin; Tairin, Suraiya

doi:10.1007/978-981-13-1501-5_74

Cited by 10 publications

(3 citation statements)

References 16 publications

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“…The existing traditional methods are focused on collision avoidance due to the over-speed in the speed-restricted zones automatically. Deep neural network architecture based techniques have been proposed in [6] to recognize traffic signs so that the road accidents can be avoided to a larger extent. Another approach [7] utilizes an embedded system, the RF transmitter, and receiver modules.…”

Section: Proposed Methodologymentioning

confidence: 99%

CNN Based Automatic Speed and Steering Control System Using Matlab

Acharya¹,

Krishna²,

Reddy³

et al. 2022

ECS Trans.

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The current technology revolution in automobile industry is the autonomous vehicle wherein the vehicle without any intervention from the human driver responds to all kind of external conditions and performs all necessary functions that a human driver would perform while driving. Traffic sign identification is a crucial task in an autonomous driving system and even a minute misinterpretation could turn into a fatal one. This paper proposes a CNN based image classification which specifically uses pre-trained feature generated neural networks present in GoogleNet. GoogleNet is a CNN which recognizes the traffic signboards present by the roadside and sends the control signal to a microcontroller (Arduino Board) which controls the speed and the steering of the vehicle. The implemented CNN based automatic speed and steering control has been verified with a greater accuracy. The work presented in this paper leads towards the development of smart and sustainable cities under the Smart Cities Mission of India.

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Section: Proposed Methodologymentioning

confidence: 99%

CNN Based Automatic Speed and Steering Control System Using Matlab

Acharya¹,

Krishna²,

Reddy³

et al. 2022

ECS Trans.

View full text Add to dashboard Cite

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“…In the case of detection, YOLO (You Only Look Once), and ulterior versions, is a common component for fast detection [181]- [183]. In general, efficient NN techniques, such as efficient modules or quantization are being incorporated incrementally to TSD and TSR networks [184]- [186] and networks are being, in an easily increasing manner, ported to hardware accelerator platform [187], [188]. For the interested reader, there are reviews available in the literature [152], [153], [164].…”

Section: E Traffic Sign Detection and Recognitionmentioning

confidence: 99%

A Survey of FPGA-Based Vision Systems for Autonomous Cars

et al. 2022

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On the road to making self-driving cars a reality, academic and industrial researchers are working hard to continue to increase safety while meeting technical and regulatory constraints Understanding the surrounding environment is a fundamental task in self-driving cars. It requires combining complex computer vision algorithms. Although state-of-the-art algorithms achieve good accuracy, their implementations often require powerful computing platforms with high power consumption. In some cases, the processing speed does not meet real-time constraints. FPGA platforms are often used to implement a category of latency-critical algorithms that demand maximum performance and energy efficiency. Since self-driving car computer vision functions fall into this category, one could expect to see a wide adoption of FPGAs in autonomous cars. In this paper, we survey the computer vision FPGA-based works from the literature targeting automotive applications over the last decade. Based on the survey, we identify the strengths and weaknesses of FPGAs in this domain and future research opportunities and challenges.INDEX TERMS autonomous car, feature extraction, field programmable gate arrays, image classification, object detection, reconfigurable architectures Self-driving functions can be implemented in several computing platforms such as ASICs, CPUs, DSPs, GPUs, FPGAs, or in heterogeneous platforms composed by any

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“…O artigo obteve os melhores resultados de classificação com o dataset alemão GTSRB, mas o mesmo não generaliza bem para outros datasets. Já em [13] foi proposto três CNNs para classificação de sinais de trânsito. As redes usadas foram: uma rede padrão GoogLeNet [14], uma rede padrão VGG [15] e uma terceira que escolhe qual das duas redes anteriores vai ser usada na classificação de uma imagem a partir das características das imagens.…”

Section: Trabalhos Relacionadosunclassified

Classificação de Sinais de Trânsito para Direção Autônoma via Rede Neural Convolucional

Oliveira¹,

Moura²,

Campos³

2020

Anais Estendidos Do X Simpósio Brasileiro De Engenharia De Sistemas Computacionais (SBESC Estendido 2020)

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Sinais de trânsito são elementos fundamentais que servem de alerta e orientação à direção autônoma de veículos. Nesse sentido, um dos principais problemas de pesquisa abordados na literatura relacionada é o de classificação automática de sinais de trânsito. Uma vez que veículos autônomos podem ser utilizados em diferentes países, onde os sinais de trânsito podem ter diferentes símbolos e significados, e necessário que os classificadores sejam capazes de se adaptar de acordo com o conjunto de sinais de cada país. Dado isso, este trabalho propõe o treinamento de modelos de aprendizado de máquina utilizando datasets públicos de placas de trânsito de diferentes países, com o objetivo de gerar classificadores suficientemente robustos para utiliza-ção nesses diversos países. Assim sendo, são apresentadas algumas estratégias para treinamento dos modelos de classificação, bem como uma avaliação experimental dessas diferentes estratégias, sendo destacada aquela que levou aos melhores resultados.

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An Efficient Traffic Sign Recognition Approach Using a Novel Deep Neural Network Selection Architecture

Cited by 10 publications

References 16 publications

CNN Based Automatic Speed and Steering Control System Using Matlab

CNN Based Automatic Speed and Steering Control System Using Matlab

A Survey of FPGA-Based Vision Systems for Autonomous Cars

Classificação de Sinais de Trânsito para Direção Autônoma via Rede Neural Convolucional

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