This paper presents a neural network approach to classify traffic signs based on greyscale images. The developed system runs on a multi-core processor. The optimization of the neural network concerning fix-point arithmetic and memory consumption results in real-time implementation without the requirement of an external memory (low system costs). A parallelization of the processing scheme allows a high utilization of the multi-core processor. The neural network proposed in this paper is trained with computer generated samples of traffic signs. These patterns cover most possible distortions and main environment situations.
Classification of traffic signs in real-time on a multi-core processor
2008 IEEE Intelligent Vehicles Symposium ; 313-318
2008-06-01
940439 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Classification of Traffic Signs in Real-Time on a Multi-Core Processor
British Library Conference Proceedings | 2008
|Real-Time Detection of Traffic Signs on a Multi-Core Processor
British Library Conference Proceedings | 2008
|Real time road signs classification
IEEE | 2008
|Classification of Traffic Signs
Springer Verlag | 2017
|