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Unconstrained Road Marking Recognition with Generative Adversarial Networks
Recent road marking recognition has achieved great success in the past few
years along with the rapid development of deep learning. Although considerable
advances have been made, they are often over-dependent on unrepresentative
datasets and constrained conditions. In this paper, to overcome these
drawbacks, we propose an alternative method that achieves higher accuracy and
generates high-quality samples as data augmentation. With the following two
major contributions: 1) The proposed deblurring network can successfully
recover a clean road marking from a blurred one by adopting generative
adversarial networks (GAN). 2) The proposed data augmentation method, based on
mutual information, can preserve and learn semantic context from the given
dataset. We construct and train a class-conditional GAN to increase the size of
training set, which makes it suitable to recognize target. The experimental
results have shown that our proposed framework generates deblurred clean
samples from blurry ones, and outperforms other methods even with unconstrained
road marking datasets.Comment: Accepted at IEEE Intelligent Vehicles Symposium (IV), 201
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