Polish Road Signs Detection and Classification System Based on Sign Sketches and ConvNet
Łukasz Chechliński , Bartłomiej Chechliński
AbstractIn this paper, we present a novel approach to detection and classification of road traffic signs. Detection and classification is performed simultaneously by the Deep Convolutional Neural Network, based on the architecture of VGG Net. Classifier is trained with the usage of sign sketches, obtained directly from the Polish Highway Code. All 169 simple signs are used. The system was tested on 100 images obtained from Google Street View. The re-view of related work shows that our system does not reach the state-of-the-art results yet, but it is much easily scalable and adaptable to the new high-way codes.
|Publication size in sheets||0.5|
|Book||Březina Tomáš, Jabłoński Ryszard (eds.): Mechatronics 2017: Recent Technological and Scientific Advances, Advances in Intelligent Systems and Computing, vol. 644, 2018, Springer International Publishing, ISBN 978-3-319-65959-6, 753 p., DOI:10.1007/978-3-319-65960-2|
|Keywords in English||traffic signs, Deep Learning, ConvNet, synthetic training base, sign detection|
|Score|| = 15.0, 25-09-2019, BookChapterSeriesAndMatConfByConferenceseries|
= 15.0, 25-09-2019, BookChapterSeriesAndMatConfByConferenceseries
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