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• W2012742472 abstract "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 1162 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." @default.
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• W2012742472 date "2014-10-01" @default.
• W2012742472 modified "2023-10-06" @default.
• W2012742472 title "Traffic Sign Recognition With Hinge Loss Trained Convolutional Neural Networks" @default.
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• W2012742472 doi "https://doi.org/10.1109/tits.2014.2308281" @default.
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