What question did this study set out to answer?

This research aims to improve traffic sign classification accuracy utilizing neural networks.

July 1, 2011

A committee of neural networks for traffic sign classification

Key Points

This research aims to improve traffic sign classification accuracy utilizing neural networks.
Developed a neural network approach that includes Convolutional Neural Networks (CNN) and Multi-Layer Perceptrons (MLP).
Used GPU implementation for fast processing and trained networks in a supervised manner.
Achieved further improvement through a committee approach combining multiple neural networks.
Initial recognition rate of 98.98% improved to 99.15% with further training.
The system outperformed human recognition rates in traffic sign classification.

Abstract

We describe the approach that won the preliminary phase of the German traffic sign recognition benchmark with a better-than-human recognition rate of 98.98%.We obtain an even better recognition rate of 99.15% by further training the nets. Our fast, fully parameterizable GPU implementation of a Convolutional Neural Network does not require careful design of pre-wired feature extractors, which are rather learned in a supervised way. A CNN/MLP committee further boosts recognition performance.

Connected Papers

Building similarity graph...

Analyzing shared references across papers

Discussion

Authors

Dan Cireşan

University of Applied Sciences and Arts of Southern Switzerland

Ueli Meier

University of Bern

Jonathan Masci

University of Applied Sciences and Arts of Southern Switzerland

Actions

Institutions

Dalle Molle Institute for Artificial Intelligence Research

University of Applied Sciences and Arts of Southern Switzerland

References and Citations

Connected Papers

Building similarity graph...

Analyzing shared references across papers

Discussion

Cite this study

Cireşan et al. (Fri,) studied this question.

synapsesocial.com/papers/69d908da183921ebcaae4776 — DOI: https://doi.org/10.1109/ijcnn.2011.6033458

Also consider

Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context:

Flexible, high performance convolutional neural networks for image classification· 2011 · 1,207 citations
The German Traffic Sign Recognition Benchmark: A multi-class classification competition· 2011 · 1,106 citations
Semilinear Predictability Minimization Produces Well-Known Feature Detectors· 1996 · 48 citations
Gradient-based learning applied to document recognition· 1998 · 58,000 citations

Also consider

Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context:

Flexible, high performance convolutional neural networks for image classification· 2011 · 1,207 citations
The German Traffic Sign Recognition Benchmark: A multi-class classification competition· 2011 · 1,106 citations
Semilinear Predictability Minimization Produces Well-Known Feature Detectors· 1996 · 48 citations
Gradient-based learning applied to document recognition· 1998 · 58,000 citations

A committee of neural networks for traffic sign classification

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Authors

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion

Cite this study

Also consider

Also consider