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Autonomous Driving: Traffic Sign Classification
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Autonomous Driving and Advance Driver Assistance Systems (ADAS) are revolutionizing the way we drive and the future of mobility. Among ADAS, Traffic Sign Classification is an important technique which assists the driver to easily interpret traffic signs on the road. In this thesis, we used the powerful combination of Image Processing and Deep Learning to pre-process and classify the traffic signs. Recent studies in Deep Learning show us how good a Convolutional Neural Network (CNN) is for image classification and there are several state-of-the-art models with classification accuracies over 99 % existing out there. This shaped our thesis to focus more on tackling the current challenges and some open-research cases. We focussed more on performance tuning by modifying the existing architectures with a trade-off between computations and accuracies. Our research areas include enhancement in low light/noisy conditions by adding Recurrent Neural Network (RNN) connections, and contribution to a universal-regional dataset with Generative Adversarial Networks (GANs). The results obtained on the test data are comparable to the state-of-the-art models and we reached accuracies above 98% after performance evaluation in different frameworks

Place, publisher, year, edition, pages
2019. , p. 53
Keywords [en]
Autonomous Driving, Deep Learning, Image Processing, Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:bth-17783OAI: oai:DiVA.org:bth-17783DiVA, id: diva2:1302763
Subject / course
ET2566 Master's Thesis (120 credits) in Electrical Engineering with emphasis on Signal processing
Educational program
ETASB Master of Science Programme in Electrical Engineering with emphasis on Signal Processing
Presentation
2019-03-06, J3506, BTH, Karlskrona, 13:00 (English)
Supervisors
Examiners
Available from: 2019-04-11 Created: 2019-04-05 Last updated: 2019-04-11Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf