2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI)
Traffic Sign Recognition on Video Sequence using Deep Neural Networks and Matching Algorithm
1
Laboratory of Cognitive Dynamic Systems, National Research University, Dolgoprudny, Russia
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2
Laboratory of Intelligent Transport, National Research University, Dolgoprudny, Russia
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Publication type: Proceedings Article
Publication date: 2019-09-01
Journal:
2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI)
Quartile SCImago
— Quartile WOS
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Impact factor: —
ISSN:
Abstract
The paper analyzes data sets containing images with labeled traffic signs, as well as modern approaches for their detection and classification on images of urban scenes. Particular attention is paid to the recognition of Russian types of traffic signs. Various modern architectures of deep neural networks for the simultaneous object detection and classification were studied, including Faster R-CNN, Mask R-CNN, Cascade R-CNN, RetinaNet. To increase the efficiency of neural network recognition of objects in a video sequence, the Seq-BBox Matching algorithm is used. Training and testing of the proposed approach was carried out on Russian Traffic Sign Dataset and IceVision Dataset containing over 150 types of road signs and more than 65,000 marked images. For all the approaches considered, quality metrics are defined: mean average precision mAP, mean average recall mAR and processing time of one frame. The highest quality performance was demonstrated by the architecture of Faster R-CNN with Seq-BBox Matching, while the highest performance is provided by the architecture of RetinaNet. Implementation was carried out using the Python 3.7 programming language and PyTorch deep learning library using NVidia CUDA technology. Performance indicators were obtained on the workstation with the NVidia Tesla V-100 32GB video card. The obtained results demonstrate the possibility of applying the proposed approach both for the resource-intensive procedure for automated labeling of road scene images for new data sets preparation, and for traffic sign recognition in on-board computer vision systems of unmanned vehicles.
Citations by journals
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1
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Journal of Physics: Conference Series
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Journal of Physics: Conference Series
1 publication, 33.33%
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Applied Intelligence
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Applied Intelligence
1 publication, 33.33%
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1
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Citations by publishers
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1
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IOP Publishing
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IOP Publishing
1 publication, 33.33%
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Springer Nature
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Springer Nature
1 publication, 33.33%
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IEEE
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IEEE
1 publication, 33.33%
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1
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Belkin I., Tkachenko S., Yudin D. Traffic Sign Recognition on Video Sequence using Deep Neural Networks and Matching Algorithm // 2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI). 2019.
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Belkin I., Tkachenko S., Yudin D. Traffic Sign Recognition on Video Sequence using Deep Neural Networks and Matching Algorithm // 2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI). 2019.
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TY - CPAPER
DO - 10.1109/IC-AIAI48757.2019.00013
UR - https://doi.org/10.1109%2FIC-AIAI48757.2019.00013
TI - Traffic Sign Recognition on Video Sequence using Deep Neural Networks and Matching Algorithm
T2 - 2019 International Conference on Artificial Intelligence: Applications and Innovations (IC-AIAI)
AU - Belkin, Ilya
AU - Tkachenko, Sergey
AU - Yudin, D.
PY - 2019
DA - 2019/09/01 00:00:00
PB - IEEE
ER -
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@inproceedings{2019_Belkin,
author = {Ilya Belkin and Sergey Tkachenko and D. Yudin},
title = {Traffic Sign Recognition on Video Sequence using Deep Neural Networks and Matching Algorithm},
year = {2019},
month = {sep},
publisher = {IEEE}
}
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