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.
Traffic Sign Recognition With Hinge Loss Trained Convolutional Neural Networks
IEEE Transactions on Intelligent Transportation Systems ; 15 , 5 ; 1991-2000
01.10.2014
1415220 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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