Randomized trial demonstrates enhanced traffic sign classification accuracy with neural networks, suggesting a powerful approach for recognition systems.
We describe the approach that won the preliminary phase of the German traffic sign recognition benchmark with a better-than-human recognition rate of 98.98%.We obtain an even better recognition rate of 99.15% by further training the nets. Our fast, fully parameterizable GPU implementation of a Convolutional Neural Network does not require careful design of pre-wired feature extractors, which are rather learned in a supervised way. A CNN/MLP committee further boosts recognition performance.
No takes yet. Share an insight, caveat, or question.
Cireşan et al. (2011) studied this question.
Synapse has enriched 3 closely related papers on similar clinical questions. Consider them for comparative context: