A committee of neural networks for traffic sign classification

Dan Cireşan, Ueli Meier, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

314 Scopus citations


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. © 2011 IEEE.
Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Number of pages4
StatePublished - Oct 24 2011
Externally publishedYes


Dive into the research topics of 'A committee of neural networks for traffic sign classification'. Together they form a unique fingerprint.

Cite this