Convolutional Neural Networks for Traffic Signs Recognition

2020 ◽  
pp. 73-91
Author(s):  
Btissam Bousarhane ◽  
Driss Bouzidi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Signs

scholarly journals Convolutional Neural Networks for Croatian Traffic Signs Recognition

2014 ◽  
Author(s):  
Vedran Vukotić ◽  
Josip Krapac ◽  
Siniša Šegvić
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Signs

scholarly journals Traffic sign recognition using convolutional neural networks

2021 ◽  
Vol 9 (2) ◽  
pp. 120-125
Author(s):  
Mutaqin Akbar
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Stochastic Gradient Descent ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Recognition ◽  
Training Stage ◽  
Testing Stage ◽  
Fully Connected

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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