Traffic Sign Recognition With Hinge Loss Trained Convolutional Neural Networks

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Traffic Sign Recognition With Hinge Loss Trained Convolutional Neural Networks

Jin, Junqi; Fu, Kun; Zhang, Changshui

IEEE transactions on intelligent transportation systems, 10/2014, Volume: 15, Issue: 5

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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.

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