Traffic sign recognition using convolutional neural networks

Jurnal Teknologi dan Sistem Komputer

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Title Traffic sign recognition using convolutional neural networks
Pengenalan rambu lalu lintas menggunakan convolutional neural networks
Creator Akbar, Mutaqin
Subject convolutional neural networks; traffic sign; sign recognition; image processing
convolutional neural networks; rambu lalu lintas; pengenalan rambu;pengolahan citra
Description Traffic sign recognition (TSR) can be used to recognize traffic signs by utilizing image processing. This paper presents traffic sign recognition in Indonesia using convolutional neural networks (CNN). The overall image dataset used is 2050 images of traffic signs, consisting of 10 kinds of signs. The CNN layer used in this study consists of one convolution layer, one pooling layer using maxpool operation, and one fully connected layer. The training algorithm used is stochastic gradient descent (SGD). At the training stage, using 1750 training images, 48 filters, and a learning rate of 0.005, the recognition results in 0.005 of loss and 100 % of accuracy. At the testing stage using 300 test images, the system recognizes the signs with 0.107 of loss and 97.33 % of accuracy.
Traffic sign recognition (TSR) digunakan mengenali rambu lalu lintas dengan memanfaatkan pengolahan citra. Artikel ini menyajikan pengenalan rambu lalu lintas di Indonesia menggunakan convolutional neural networks (CNN). Dataset citra yang digunakan secara keseluruhan adalah 2050 citra rambu lalu lintas, yang terdiri dari 10 macam rambu. Lapisan CNN yang digunakan terdiri dari satu lapisan konvolusi, satu lapisan pooling menggunakan operasi maxpool, dan satu lapisan fully-connected. Algoritme pelatihan yang digunakan adalah Stochastic Gradient Descent (SGD). Pada tahap pelatihan dengan menggunakan 1750 data citra latih, 48 filter dan laju pelatihan 0,005, dihasilkan galat 0,005 dan akurasi 100 %. Pada tahap pengujian menggunakan 300 data citra uji, sistem dapat mengenali rambu lalu lintas dengan galat 0,107 dan akurasi mencapai 97,33 %.
Publisher Departemen Teknik Komputer, Fakultas Teknik, Universitas Diponegoro
Date 2021-04-30
Type info:eu-repo/semantics/article

Format application/pdf
Source Jurnal Teknologi dan Sistem Komputer; Volume 9, Issue 2, Year 2021 (April 2021); 120-125
Jurnal Teknologi dan Sistem Komputer; Volume 9, Issue 2, Year 2021 (April 2021); 120-125
Language ind
Rights Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro

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