Portable Convolution Neural Networks for Traffic Sign Recognition in Intelligent Transportation Systems
Chapter, Peer reviewed
Accepted version
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Date
2019Metadata
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Abstract
Deep convolutional neural networks (CNN) have the
strength in traffic-sign classification in terms of high accuracy.
However, CNN models usually contains multiple layers with a
large number of parameters consequently leading to a large model
size. The bulky model size of CNN models prevents them from
the wide deployment in mobile and portable devices in Intelligent
Transportation Systems. In this paper, we design and develop a
portable convolutional neural network (namely portable CNN)
structure used for traffic-sign classification. This portable CNN
model contains a stacked convolutional structure consisting of
factorization and compression modules. We conducted extensive
experiments to evaluate the performance of the proposed Portable
CNN model. Experimental results show that our model has
the advantages of smaller model size while maintaining high
classification accuracy, compared with conventional CNN models