TY - GEN
T1 - Traffic sign recognition with multi-scale convolutional networks
AU - Sermanet, Pierre
AU - Lecun, Yann
PY - 2011
Y1 - 2011
N2 - We apply Convolutional Networks (ConvNets) to the task of traffic sign classification as part of the GTSRB competition. ConvNets are biologically-inspired multi-stage architectures that automatically learn hierarchies of invariant features. While many popular vision approaches use hand-crafted features such as HOG or SIFT, ConvNets learn features at every level from data that are tuned to the task at hand. The traditional ConvNet architecture was modified by feeding 1nd stage features in addition to 2nd stage features to the classifier. The system yielded the 2nd-best accuracy of 98.97% during phase I of the competition (the best entry obtained 98.98%), above the human performance of 98.81%, using 3232 color input images. Experiments conducted after phase 1 produced a new record of 99.17% by increasing the network capacity, and by using greyscale images instead of color. Interestingly, random features still yielded competitive results (97.33%).
AB - We apply Convolutional Networks (ConvNets) to the task of traffic sign classification as part of the GTSRB competition. ConvNets are biologically-inspired multi-stage architectures that automatically learn hierarchies of invariant features. While many popular vision approaches use hand-crafted features such as HOG or SIFT, ConvNets learn features at every level from data that are tuned to the task at hand. The traditional ConvNet architecture was modified by feeding 1nd stage features in addition to 2nd stage features to the classifier. The system yielded the 2nd-best accuracy of 98.97% during phase I of the competition (the best entry obtained 98.98%), above the human performance of 98.81%, using 3232 color input images. Experiments conducted after phase 1 produced a new record of 99.17% by increasing the network capacity, and by using greyscale images instead of color. Interestingly, random features still yielded competitive results (97.33%).
UR - http://www.scopus.com/inward/record.url?scp=80054736963&partnerID=8YFLogxK
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U2 - 10.1109/IJCNN.2011.6033589
DO - 10.1109/IJCNN.2011.6033589
M3 - Conference contribution
AN - SCOPUS:80054736963
SN - 9781457710865
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 2809
EP - 2813
BT - 2011 International Joint Conference on Neural Networks, IJCNN 2011 - Final Program
T2 - 2011 International Joint Conference on Neural Network, IJCNN 2011
Y2 - 31 July 2011 through 5 August 2011
ER -