Citation

Cireşan D, Meier U, Masci J, Schmidhuber J. Neural. Netw. 2012; 32: 333-338.

Copyright

(Copyright © 2012, Elsevier Publishing)

DOI

10.1016/j.neunet.2012.02.023

PMID

22386783

Abstract

We describe the approach that won the final phase of the German traffic sign recognition benchmark. Our method is the only one that achieved a better-than-human recognition rate of 99.46%. We use a fast, fully parameterizable GPU implementation of a Deep Neural Network (DNN) that does not require careful design of pre-wired feature extractors, which are rather learned in a supervised way. Combining various DNNs trained on differently preprocessed data into a Multi-Column DNN (MCDNN) further boosts recognition performance, making the system insensitive also to variations in contrast and illumination.

Language: en