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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.
Ach et al. (Sun,) studied this question.