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Pengenalan rambu lalu lintas menggunakan convolutional neural networks

Traffic sign recognition using convolutional neural networks

Department of Informatics, Universitas Mercu Buana Yogyakarta, Gg. Jembatan Merah No.84C, Kampus 2 UMBY, Sleman, Indonesia 55283, Indonesia

Received: 20 Oct 2020; Revised: 28 Jan 2021; Accepted: 5 Mar 2021; Published: 30 Apr 2021; Available online: 20 Apr 2021.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
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.
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Keywords: convolutional neural networks; traffic sign; sign recognition; image processing
Funding: Universitas Mercu Buana Yogyakarta

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