Improved Traffic Sign Recognition Using Deep ConvNet Architecture
| Autor: | Omar Belghaouti, Mohamed Tabaa, Wahida Handouzi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre Convolutional neural network Order (business) 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences Traffic sign recognition 020201 artificial intelligence & image processing Artificial intelligence Architecture business computer General Environmental Science |
| Zdroj: | EUSPN/ICTH |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2020.10.064 |
| Popis: | Everyday more and more accidents between vehicles accrue mainly because of poorly visible road signs, that is why it is important to have a system that can help drivers for safer driving. In order to improve road safety and significantly reduce the risk of these accidents, numerous works have been undertaken in computer vision. In this paper, we propose a contribution to this issue by taking advantage of the remarkable results of deep convolutional neural networks in computer vision. We propose an automatic recognition system of road signs based on a modified model inspired by LeNet model. The results obtained by comparison of LeNet model and two proposed modified models on the German traffic dataset is about 99% accuracy which is promising compared to the state-of-the-art results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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