Article
Exploiting synergies of mobile mapping sensors and deep learning for traffic sign recognition systems
Author/s | Arcos García, Álvaro
Soilán, Mario Álvarez García, Juan Antonio Riveiro, Belén |
Department | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Publication Date | 2017 |
Deposit Date | 2021-09-13 |
Published in |
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Abstract | This paper presents an efficient two-stage traffic sign recognition system. First, 3D point cloud data is
acquired by a LINX Mobile Mapper system and processed to automatically detect traffic signs based on
their ... This paper presents an efficient two-stage traffic sign recognition system. First, 3D point cloud data is acquired by a LINX Mobile Mapper system and processed to automatically detect traffic signs based on their retro-reflective material. Then, classification is carried out over the point cloud projection on RGB images applying a Deep Neural Network which comprises convolutional and spatial transformer layers. This network is evaluated in three European traffic sign datasets. On the GTSRB, it outperforms previous state-of-the-art published works and achieves top-1 rank with an accuracy of 99.71%. Furthermore, a Spanish traffic sign recognition dataset is released. |
Funding agencies | Ministerio de Economía y Competitividad (MINECO). España |
Project ID. | TIN2013-46801-C4-1-R
TIN2013-46801-C4-4-R |
Citation | Arcos García, Á., Soilán, M., Álvarez García, J.A. y Riveiro, B. (2017). Exploiting synergies of mobile mapping sensors and deep learning for traffic sign recognition systems. Expert Systems With Applications, 89, 286-295. |
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