期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 15, 期 5, 页码 1991-2000出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2014.2308281
关键词
Convolutional neural networks (CNNs); hinge loss; stochastic gradient descent (SGD); traffic sign recognition (TSR)
资金
- 973 Program [2013CB329503]
- National Natural Science Foundation of China [91120301]
- Beijing Municipal Education Commission Science and Technology Development [KZ201210005007]
Traffic sign recognition (TSR) is an important and challenging task for intelligent transportation systems. We describe the details of our model's architecture for TSR and suggest a hinge loss stochastic gradient descent (HLSGD) method to train convolutional neural networks (CNNs). Our CNN consists of three stages (70-110-180) with 1 162 284 trainable parameters. The HLSGD is evaluated on the German Traffic Sign Recognition Benchmark, which offers a faster and more stable convergence and a state-of-the-art recognition rate of 99.65%. We write a graphics processing unit package to train several CNNs and establish the final classifier in an ensemble way.
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