A Method for Traffic Sign Recognition with CNN using GPU

Shustanov, Alexander; Yakimov, Pavel

doi:10.5220/0006436100420047

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…The architecture of our model is: 1) 2 Conv2D layer (filter=32, kernel_size= (5,5), activation="relu") 2) MaxPool2D layer ( pool_size=(2,2)) 3) Dropout layer (rate=0.25) 4) 2 Conv2D layer (filter=64, kernel_size= (3,3), activation="relu") 5) MaxPool2D layer ( pool_size=(2,2)) 6) Dropout layer (rate=0.25) 7) Flatten layer to squeeze the layers into 1 dimension 8) Dense Fully connected layer (256 nodes, activation="relu") 9) Dropout layer (rate=0.5) 10) Dense layer (43 nodes, activation="softmax") Model is compiled with Adam optimizer which performs well and loss is "categorical_crossentropy" because we have multiple classes to categorise.…”

Section: B Build a Cnn Modelmentioning

confidence: 99%

“…And then build the GUI for uploading the image and a button is used to classify which calls the classify() function. The classify () function is converting the image into the dimension of shape (1,30,30,3). This is because to predict the traffic sign , same dimension which was used when building the model is provided.…”

Section: A Build Ing a Graphical User Interface (Gui)mentioning

confidence: 99%

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Traffic Sign Recognition using CNN and Keras

Sahu¹

2021

IJRASET

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Detection and recognition of traffic signs is very important and could potentially be used for driver assistance to reduce accidents and eventually in driverless automobiles.Also traffic signs are essential part of day to day lives. They contain critical information that ensures the safety of all the people . As there are number of traffic signs throughout the world , it is almost impossible for human beings to remember them and identity their meaning which create huge traffic accidents and human loss throughout the world so it is important to establish this project that will remember the traffic signs of all the country throughout the world. Traffic signs classification is the process of identifying which class a traffic sign belongs to. In this project with the help of deep learning , different traffic signs are identified and classified into different categories which helps in reducing various traffic accidents and also reduces human time to remember different traffic signs . In this paper , traffic sign recognition using Convolutional Neural Network(CNN) is implemented ,the CNN will be trained by using GTSRB dataset of 43 different classes containing 50,000 images of traffic signsThe results will show 94% accuracy.

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Section: B Build a Cnn Modelmentioning

confidence: 99%

Section: A Build Ing a Graphical User Interface (Gui)mentioning

confidence: 99%

Traffic Sign Recognition using CNN and Keras

Sahu¹

2021

IJRASET

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“…Hu et al 12 introduced branch convolution neural network with a detailed process to change a regular pre-trained convolution neural network (CNN) into a branch convolution neural network. Shustanov and Yakimov 13 presented a CNN architecture, executed in real time on a mobile GPU. Yang et al 14 proposed a scale invariant architecture.…”

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confidence: 99%

Traffic sign recognition on Indian database using wavelet descriptors and convolutional neural network ensemble

Sanyal

Mohapatra

Dash

2022

Concurrency and Computation

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Traffic sign recognition (TSR) has been a rising and lucrative field for researchers during the last decades. The high improvement of ADAS (autonomic driving autonomous system) has led researchers worldwide to concentrate on the development of TSR systems. As such, a novel automated multiclass TSR system is proposed. The architecture uses wavelet descriptors to extract the high information density and the traffic signs' edges and curves. The LL band image is directly fed into the classifier to avoid normalization. Three classifiers, CNN, CNN ensemble, and LSTM, are deployed for recognition. The architecture is implemented on IRSDBv1.0, the first available Indian traffic sign database. The architecture is also implemented on the standard traffic sign database GTSRB to investigate its effectiveness. An efficiency of 71.57% and 96.76% are recorded on IRSDBv1.0, and GTSRB, respectively. A list of comparative results is also provided to prove the competence of the architecture. The reasons behind the difference in the achieved accuracy are also discussed.

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“…The widely used dataset in the literature for the purpose to train the TSR model are such the German Traffic Sign Detection Benchmark (GTSDB) and the Malaysian Traffic Signs Dataset (MTSD). For instance, the GTSDB is used in [6][7][8][9]. On the other hand, researches in [10][11][12] used the MTSD dataset for the implementation of the TSR.…”

mentioning

confidence: 99%

“…Studies in [12,13] applied the established Artificial Neural Network (ANN) in TSR program which yields to 97.00% and 99.00% accuracy rate respectively. Whereas, the Convolutional Neural Network (CNN) has been implemented in [6,7,14] and obtained the accuracy rate of above 95.00%.…”

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confidence: 99%

A Real-Time Malaysian Traffic Sign Recognition Using YOLO Algorithm

Mangshor

Paudzi

Ibrahim

et al. 2021

Lecture Notes in Electrical Engineering

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In recent years, research towards Autonomous Vehicle (AV) has grown up tremendously. The introduction of Advanced Driving Assistance System (ADAS) in AV has led researchers to explore on the key functionalities that an AV should have, proposes to create a convenience and safe environment where there is no input acquire from a human throughout the journey. One of the key functionalities is the Traffic Sign Recognition (TSR). Hence, this study aims to develop a real-time traffic sign recognition system that can recognize Malaysian traffic sign in real-time and to test the accuracy of the system. The Deep Learning technology using You Only Look Once (YOLO) algorithm is used to train the model for recognition. Five types of warning traffic sign including crossroad, crossroad right, crossroad left, school children crossing and bump from Malaysian traffic sign is used. The test results reached for real-time object detection is 96.00% accuracy on both traffic sign detection and recognition. The finding from this study is believed to be helpful as it may contribute to the ADAS development in the automotive industry for the self-driving vehicle. KeywordsTraffic sign recognition • Autonomous vehicle • YOLO 1 Introduction Nowadays, the automotive industries are vigorously exploring Autonomous Vehicle (AV) which includes the self-driving programs [1]. This technology has been growing instantaneously throughout the world [2]. The AV is a vehicle that the computers take the lead the art of self-driving program [3] using the integration of Advanced Driving

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A Method for Traffic Sign Recognition with CNN using GPU

Cited by 8 publications

References 7 publications

Traffic Sign Recognition using CNN and Keras

Traffic Sign Recognition using CNN and Keras

Traffic sign recognition on Indian database using wavelet descriptors and convolutional neural network ensemble

A Real-Time Malaysian Traffic Sign Recognition Using YOLO Algorithm

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