Multi-column deep neural network for traffic sign classification

Neural Netw. 2012 Aug;32:333-8. doi: 10.1016/j.neunet.2012.02.023. Epub 2012 Feb 14.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Automobile Driving / psychology*
  • Computer Graphics
  • Electronic Data Processing
  • Motor Vehicles
  • Neural Networks, Computer*
  • Pattern Recognition, Automated*
  • Vision, Ocular / physiology*