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Traffic Sign Classification Using Convolutional Neural Network
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In today's world, deep learning fields are getting boosted with increasing speed. Lot of innovations and different algorithms are being developed. In field of computer vision, related to autonomous driving sector, traffic signs play an important role to provide real time data of an environment. Different algorithms were developed to classify these Signs. But performance still needs to improve for real time environment. Even the computational power required to train such model is high. In this paper, Convolutional Neural Network model is used to Classify Traffic Sign. The experiments are conducted on a real-world data set with images and videos captured from ordinary car driving as well as on GTSRB dataset [15] available on Kaggle. This proposed model is able to outperform previous models and resulted with accuracy of 99.6% on validation set. This idea has been granted Innovation Patent by Australian IP to Authors of this Research Paper. [24]
Title: Traffic Sign Classification Using Convolutional Neural Network
Description:
In today's world, deep learning fields are getting boosted with increasing speed.
Lot of innovations and different algorithms are being developed.
In field of computer vision, related to autonomous driving sector, traffic signs play an important role to provide real time data of an environment.
Different algorithms were developed to classify these Signs.
But performance still needs to improve for real time environment.
Even the computational power required to train such model is high.
In this paper, Convolutional Neural Network model is used to Classify Traffic Sign.
The experiments are conducted on a real-world data set with images and videos captured from ordinary car driving as well as on GTSRB dataset [15] available on Kaggle.
This proposed model is able to outperform previous models and resulted with accuracy of 99.
6% on validation set.
This idea has been granted Innovation Patent by Australian IP to Authors of this Research Paper.
[24].
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