Multi-column deep neural network for traffic sign classification
Multi-column deep neural network for traffic sign classification
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DOI：
10.1016/j.neunet.2012.02.023
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发表时间：
2012-08-01
期刊：
影响因子：
7.8
通讯作者：
Schmidhuber, Juergen
中科院分区：
文献类型：
--
作者：
Ciresan, Dan;Meier, Ueli;Schmidhuber, Juergen
我们描述的方法，赢得了德国交通标志识别基准的最后阶段。我们的方法是唯一一个实现了99.46%的优于人类的识别率。我们使用一种快速、完全可参数化的GPU实现深度神经网络（DNN），不需要精心设计预连接的特征提取器，这些特征提取器是以监督的方式学习的。将在不同预处理数据上训练的各种DNN组合到多列DNN（MCDNN）中，进一步提高了识别性能，使系统对对比度和照明的变化也不敏感。(C)2012爱思唯尔有限公司保留所有权利。
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 better-than-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. (C) 2012 Elsevier Ltd. All rights reserved.