Traffic sign recognition using convolutional neural networks
| Autor: | Afaf Bouhoute, Ismail Berrada, Kaoutar Sefrioui Boujemaa, Karim Boubouh |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
business.industry
Computer science 010401 analytical chemistry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Advanced driver assistance systems 02 engineering and technology 01 natural sciences Convolutional neural network Field (computer science) 0104 chemical sciences Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Traffic sign recognition 020201 artificial intelligence & image processing Segmentation Artificial intelligence business |
| Zdroj: | WINCOM |
| DOI: | 10.1109/wincom.2017.8238205 |
| Popis: | Traffic sign recognition (TSR) represents an important feature of advanced driver assistance systems, contributing to the safety of the drivers, pedestrians and vehicles as well. Developing TSR systems requires the use of computer vision techniques, which could be considered fundamental in the field of pattern recognition in general. Despite all the previous works and research that has been achieved, traffic sign detection and recognition still remain a very challenging problem, precisely if we want to provide a real time processing solution. In this paper, we present a comparative and analytical study of the two major approaches for traffic sign detection and recognition. The first approach is based on the color segmentation technique and convolutional neural networks (C-CNN), while the second one is based on the fast region-based convolutional neural networks approach (Fast R-CNN). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|