Malaysia traffic sign recognition with convolutional neural network

Lau, Mian Mian; Lim, King Hann; Gopalai, Alpha Agape

doi:10.1109/icdsp.2015.7252029

Cited by 36 publications

(18 citation statements)

References 18 publications

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“…Li et al [18] employ the PHOG descriptor, a variant of the HOG descriptor. Other descriptors are based upon the discrete Fourier transform [26], the Hough transform [23], the SURF method [30], the values of the neighboring pixels in a ROI [31], or predefined contour descriptors for basic shapes (circular, triangular, or rectangular) [27]. …”

Section: State-of-the-artmentioning

confidence: 99%

Traffic Sign Detection System for Locating Road Intersections and Roundabouts: The Chilean Case

Villalón-Sepúlveda

Torres-Torriti

Flores-Calero

2017

Sensors

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This paper presents a traffic sign detection method for signs close to road intersections and roundabouts, such as stop and yield (give way) signs. The proposed method relies on statistical templates built using color information for both segmentation and classification. The segmentation method uses the RGB-normalized (ErEgEb) color space for ROIs (Regions of Interest) generation based on a chromaticity filter, where templates at 10 scales are applied to the entire image. Templates consider the mean and standard deviation of normalized color of the traffic signs to build thresholding intervals where the expected color should lie for a given sign. The classification stage employs the information of the statistical templates over YCbCr and ErEgEb color spaces, for which the background has been previously removed by using a probability function that models the probability that the pixel corresponds to a sign given its chromaticity values. This work includes an analysis of the detection rate as a function of the distance between the vehicle and the sign. Such information is useful to validate the robustness of the approach and is often not included in the existing literature. The detection rates, as a function of distance, are compared to those of the well-known Viola–Jones method. The results show that for distances less than 48 m, the proposed method achieves a detection rate of 87.5% and 95.4% for yield and stop signs, respectively. For distances less than 30 m, the detection rate is 100% for both signs. The Viola–Jones approach has detection rates below 20% for distances between 30 and 48 m, and barely improves in the 20–30 m range with detection rates of up to 60%. Thus, the proposed method provides a robust alternative for intersection detection that relies on statistical color-based templates instead of shape information. The experiments employed videos of traffic signs taken in several streets of Santiago, Chile, using a research platform implemented at the Robotics and Automation Laboratory of PUC to develop driver assistance systems.

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Section: State-of-the-artmentioning

confidence: 99%

Traffic Sign Detection System for Locating Road Intersections and Roundabouts: The Chilean Case

Villalón-Sepúlveda

Torres-Torriti

Flores-Calero

2017

Sensors

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“…En el primer caso, entre los algoritmos preferidos están: SVM (Hastie et al, 2009), usado en los trabajos de Greenhalgh et al (Greenhalgh and Mirmehdi, 2012), Salti et al(Salti et al, 2015), Zaklouta et al (Zaklouta and Stanciulescu, 2012), Li et al (Li et al, 2015), (Lillo et al, 2015), Lillo et al (Fleyeh et al, 2013), k−NN (Hastie et al, 2009) en las investigaciones de Han et al (Han et al, 2015), redes neuronales artificiales, empleadas por Huang et al (Huang et al, 2014) con el caso ELM, Perez et al (Perez-Perez et al, 2013) con la implementación MLP y Lau et al (Lau et al, 2015) experimentando con CNN y RBNN.…”

Section: Reconocimientounclassified

“…Perez et al (Perez-Perez et al, 2013) usaron PCA para la reducción de la dimensión y la elección de características. Finalmente, Lau et al (Lau et al, 2015) usaron una ponderación de los píxeles vecinos de la ROI.…”

Section: Reconocimientounclassified

“…Las principales bases de datos corresponden a países como Alemania (Salti et al, 2015;Stanciulescu, 2012, 2014;Huang et al, 2014;Perez-Perez et al, 2013), Reino Unido (Greenhalgh and Mirmehdi, 2012), España (Lillo et al, 2015;Carrasco, 2009), Japón (Nguyen et al, 2014) o Malaysian (Lau et al, 2015). En las señales de tráfico predominan los colores rojo, amarillo, verde y azul; y las formas circular, triangular, cuadrada, rectangular, flechas, diamante y semi-circular.…”

Section: Bases De Datosunclassified

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Sistema de detección de señales de tráfico para la localización de intersecciones viales y frenado anticipado

Villalón-Sepúlveda

Torres-Torriti

Flores-Calero

2017

Revista Iberoamericana de Automática e Informática Industrial R

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“…A classical architecture of convolutional neural network was first proposed by Lecun et al [29,30]. As a kind of deep learning neural network, several powerful applications of CNNs were reported including pattern recognition and classification, such as human face recognition [31], traffic sign recognition [32], and object recognition [33]. Recently, in the field of image classification accuracy, convolution neural network (CNN) achieved a state-of-the art result, which can classify more than a million images into 1000 different classes [29,34,35].…”

Section: Cnn Conceptsmentioning

confidence: 99%

Memristor Neural Network Design

Huang¹,

Zhang²,

Li³

et al. 2018

Memristor and Memristive Neural Networks

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Neural network, a powerful learning model, has archived amazing results. However, the current Von Neumann computing system-based implementations of neural networks are suffering from memory wall and communication bottleneck problems ascribing to the Complementary Metal Oxide Semiconductor (CMOS) technology scaling down and communication gap. Memristor, a two terminal nanosolid state nonvolatile resistive switching, can provide energy-efficient neuromorphic computing with its synaptic behavior. Crossbar architecture can be used to perform neural computations because of its high density and parallel computation. Thus, neural networks based on memristor crossbar will perform better in real world applications. In this chapter, the design of different neural network architectures based on memristor is introduced, including spiking neural networks, multilayer neural networks, convolution neural networks, and recurrent neural networks. And the brief introduction, the architecture, the computing circuits, and the training algorithm of each kind of neural networks are presented by instances. The potential applications and the prospects of memristor-based neural network system are discussed.

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Malaysia traffic sign recognition with convolutional neural network

Cited by 36 publications

References 18 publications

Traffic Sign Detection System for Locating Road Intersections and Roundabouts: The Chilean Case

Traffic Sign Detection System for Locating Road Intersections and Roundabouts: The Chilean Case

Sistema de detección de señales de tráfico para la localización de intersecciones viales y frenado anticipado

Memristor Neural Network Design

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