A committee of neural networks for traffic sign classification
Published 1 July 2011
Dan Cireşan, Ueli Meier, Jonathan Masci, Jürgen Schmidhuber
Citations375
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
This work describes the approach that won the preliminary phase of the German traffic sign recognition benchmark with a better-than-human recognition rate, and obtains an even better recognition rate by further training the nets.
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.
Keywords
Computer Science
Proceedings of the IEEEGradient-based learning applied to document recognition
58,219 Citations1998Yann LeCun, Léon Bottou +2 more
This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task, and Convolutional neural networks are shown to outperform all other techniques.
The Journal of PhysiologyReceptive fields of single neurones in the cat's striate cortex
4,691 Citations1959David H. Hubel, T. N. Wiesel
The present investigation, made in acute preparations, includes a study of receptive fields of cells in the cat's striate cortex, which resembled retinal ganglion-cell receptive fields, but the shape and arrangement of excitatory and inhibitory areas differed strikingly from the concentric pattern found in retinalganglion cells.
Vision ResearchSparse coding with an overcomplete basis set: A strategy employed by V1?
3,720 Citations1997Bruno A. Olshausen, David J. Field
These deviations from linearity provide a potential explanation for the weak forms of non-linearity observed in the response properties of cortical simple cells, and they further make predictions about the expected interactions among units in response to naturalistic stimuli.
Nature NeuroscienceHierarchical models of object recognition in cortex
3,404 Citations1999Maximilian Riesenhuber, Tomaso Poggio
A new hierarchical model consistent with physiological data from inferotemporal cortex that accounts for this complex visual task and makes testable predictions is described.
Best practices for convolutional neural networks applied to visual document analysis
2,815 Citations2005P. Simard, Don Steinkraus +1 more
A set of concrete bestpractices that document analysis researchers can use to get good results with neural networks, including a simple "do-it-yourself" implementation of convolution with a flexible architecture suitable for many visual document problems.
Lecture notes in computer scienceEvaluation of Pooling Operations in Convolutional Architectures for Object Recognition
1,518 Citations2010Dominik Scherer, Andreas Müller +1 more
The aim is to gain insight into different functions by directly comparing them on a fixed architecture for several common object recognition tasks, and empirical results show that a maximum pooling operation significantly outperforms subsampling operations.
International Joint Conference on Artificial IntelligenceFlexible, high performance convolutional neural networks for image classification
1,203 Citations2011Dan Cireşan, Ueli Meier +3 more
The German Traffic Sign Recognition Benchmark: A multi-class classification competition
1,074 Citations2011Johannes Stallkamp, Marc Schlipsing +2 more
The “German Traffic Sign Recognition Benchmark” is a multi-category classification competition held at IJCNN 2011, and a comprehensive, lifelike dataset of more than 50,000 traffic sign images has been collected.
Neural ComputationDeep, Big, Simple Neural Nets for Handwritten Digit Recognition
1,064 Citations2010Dan Claudiu Cireşan, Ueli Meier +2 more
Good old online backpropagation for plain multilayer perceptrons yields a very low 0.35 error rate on the MNIST handwritten digits benchmark.
Object Recognition with Features Inspired by Visual Cortex
828 Citations2005T. Serre, Lior Wolf +1 more
The performance of the approach constitutes a suggestive plausibility proof for a class of feedforward models of object recognition in cortex and exhibits excellent recognition performance and outperforms several state-of-the-art systems on a variety of image datasets including many different object categories.
High Performance Convolutional Neural Networks for Document Processing
431 Citations2006Kumar Chellapilla, Sidd Puri +1 more
Three novel approaches to speeding up CNNs are presented: a) unrolling convolution, b) using BLAS (basic linear algebra subroutines), and c) using GPUs (graphic processing units).
International Journal of Computer VisionObject Class Recognition and Localization Using Sparse Features with Limited Receptive Fields
377 Citations2008Jim Mutch, David Lowe
PLoS Computational BiologyA High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation
254 Citations2009Nicolas Pinto, David Doukhan +2 more
Performance and Scalability of GPU-Based Convolutional Neural Networks
242 Citations2010Daniel Strigl, Klaus Kofler +1 more
This paper presents the implementation of a framework for accelerating training and classification of arbitrary Convolutional Neural Networks (CNNs) on the GPU and describes the basic parts of a CNN and demonstrates the performance and scalability improvement that can be achieved by shifting the computation-intensive tasks of aCNN to the GPU.
Network Computation in Neural SystemsIndependent component analysis applied to feature extraction from colour and stereo images
234 Citations2000Patrik O. Hoyer, Aapo Hyvärinen
The similarities between these results and the observed properties of simple cells in the primary visual cortex are further evidence for the hypothesis that visual cortical neurons perform some type of redundancy reduction, which was one of the original motivations for ICA in the first place.
Lecture notes in computer scienceHierarchical Neural Networks for Image Interpretation
222 Citations2003Sven Behnke
The results show clear trends in the direction of improvement in the level of supervised learning in relation to the recognition of meter values and in the application of Matrix Codes.
Network Computation in Neural SystemsIndependent component analysis applied to feature extraction from colour and stereo images
115 Citations2000Patrik O. Hoyer, Aapo Hyvärinen
Large-scale object recognition with CUDA-accelerated hierarchical neural networks
81 Citations2009Rafael Uetz, Sven Behnke
This work presents a hierarchical, locally-connected neural network model that is well-suited for large-scale, high-performance object recognition and creates a massively parallel implementation of the model which is executed on a state-of-the-art graphics card.
Neural ComputationSemilinear Predictability Minimization Produces Well-Known Feature Detectors
48 Citations1996Jürgen Schmidhuber, Martin Eldracher +1 more
This paper applies semilinear PM to static real world images and finds that without a teacher and without any significant preprocessing, the system automatically learns to generate distributed representations based on well-known feature detectors, thus extracting simple features related to those considered useful for image preprocessing and compression.