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 betterthan-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.
In particular, Ciresan et al. REF won the final phase of the competition using a GPU implementation of a Deep Neural Network (DNN), further improved in a Multi-Column DNN that makes the system robust to variations in contrast and illumination.
Multi-Column Deep Neural Network for Traffic Sign Classification
Neural networks : the official journal of the International Neural Network Society
Neural networks : the official journal of the International Neural Network Society
Mathematics Computer Science Medicine