title: Multi-column deep neural network for traffic sign classification.
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.
authors.affiliation: IDSIA - USI - SUPSI — Galleria 2, Manno - Lugano 6928, Switzerland. dan@idsia.ch
authors.forename: Dan
authors.initials: D
authors.lastname: Cireşan
authors.forename: Ueli
authors.initials: U
authors.lastname: Meier
authors.forename: Jonathan
authors.initials: J
authors.lastname: Masci
authors.forename: Jürgen
authors.initials: J
authors.lastname: Schmidhuber
mesh_terms: D000465:Algorithms; D001334:Automobile Driving / Q000523:psychology*; D003196:Computer Graphics; D001330:Electronic Data Processing; D018986:Motor Vehicles; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D014785:Vision, Ocular / Q000502:physiology*