The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Traffic sign recognition with multi-scale Convolutional Networks
Abstract
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 1ststage features in addition to 2ndstage 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 32×32 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%).
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Citations
Cites
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BackgroundA survey on deep learning based bearing fault diagnosis
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Exploiting Unlabeled Data in Smart Cities using Federated Edge Learning
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Metadata
- Authors
- Sermanet, P., & LeCun, Y.
- Venue
- The 2011 International Joint Conference on Neural Networks
- Publisher
- IEEE
- Published
- 2011-07
- Pages
- 2809-2813
- ISSN
- 2161-4407 (ISSN-L)
- OpenAlex
- W2125085157
- Cited by
- 759
- References
- 2
- FWCI
- 16.98
- Type
- conference paper
- Language
- en
- Open access
- closed
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