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A committee of neural networks for traffic sign classification

A committee of neural networks for traffic sign classification,10.1109/IJCNN.2011.6033458,Dan Ciresan,Ueli Meier,Jonathan Masci,Jurgen Schmidhuber

A committee of neural networks for traffic sign classification   (Citations: 2)
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We describe the approach that won the prelimi- nary 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.
Conference: International Symposium on Neural Networks - ISNN , pp. 1918-1921, 2011
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