Improving Traffic Signs Recognition Based Region Proposal and Deep Neural Networks. Hoang, V. D., Le, M. H., Tran, T. T., & Pham, V. H. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 10752 LNAI, pages 604–613. Springer International Publishing, 2018. ISSN: 16113349![Improving Traffic Signs Recognition Based Region Proposal and Deep Neural Networks [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Nowadays, traffic sign recognition has played an important task in autonomous vehicle, intelligent transportation systems. However, it is still a challenging task due to the problems of a variety of color, shape, environmental conditions. In this paper, we propose a new approach for improving accuracy of traffic sign recognition. The contribution of this work is three-fold: First, region proposal based on segmentation technique is applied to cluster traffic signs into several sub regions depending upon the supplemental signs and the main sign color. Second, image augmentation of training dataset generates a larger data for deep neural network learning. This proposed task is aimed to address the small data problem. It is utilized for enhancing capabilities of deep learning. Finally, we design appropriately a deep neural network to image dataset, which combines the original images and proposal images. The proposed approach was evaluated on a benchmark dataset. Experimental evaluation on public benchmark dataset shows that the proposed approach enhances performance to 99.99% accuracy. Comparison results illustrated that our proposed method reaches higher performance than almost state-of-the-art methods.
@incollection{Hoang_2018,
title = {Improving {Traffic} {Signs} {Recognition} {Based} {Region} {Proposal} and {Deep} {Neural} {Networks}},
volume = {10752 LNAI},
isbn = {978-3-319-75419-2},
url = {http://dx.doi.org/10.1007/978-3-319-75420-8_57},
abstract = {Nowadays, traffic sign recognition has played an important task in autonomous vehicle, intelligent transportation systems. However, it is still a challenging task due to the problems of a variety of color, shape, environmental conditions. In this paper, we propose a new approach for improving accuracy of traffic sign recognition. The contribution of this work is three-fold: First, region proposal based on segmentation technique is applied to cluster traffic signs into several sub regions depending upon the supplemental signs and the main sign color. Second, image augmentation of training dataset generates a larger data for deep neural network learning. This proposed task is aimed to address the small data problem. It is utilized for enhancing capabilities of deep learning. Finally, we design appropriately a deep neural network to image dataset, which combines the original images and proposal images. The proposed approach was evaluated on a benchmark dataset. Experimental evaluation on public benchmark dataset shows that the proposed approach enhances performance to 99.99\% accuracy. Comparison results illustrated that our proposed method reaches higher performance than almost state-of-the-art methods.},
booktitle = {Lecture {Notes} in {Computer} {Science} (including subseries {Lecture} {Notes} in {Artificial} {Intelligence} and {Lecture} {Notes} in {Bioinformatics})},
publisher = {Springer International Publishing},
author = {Hoang, Van Dung and Le, My Ha and Tran, Truc Thanh and Pham, Van Huy},
year = {2018},
doi = {10.1007/978-3-319-75420-8_57},
note = {ISSN: 16113349},
keywords = {Data augmentation, Deep neural networks, Region proposal, Traffic sign recognition},
pages = {604--613},
}
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