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Multi-Task Learning using Road Surface Condition Classification and Road Scene Semantic Segmentation
Linköping University, Department of Biomedical Engineering.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Understanding road surface conditions is an important component in active vehicle safety. Estimations can be achieved through image classification using increasingly popular convolutional neural networks (CNNs). In this paper, we explore the effects of multi-task learning by creating CNNs capable of simultaneously performing the two tasks road surface condition classification (RSCC) and road scene semantic segmentation (RSSS). A multi-task network, containing a shared feature extractor (VGG16, ResNet-18, ResNet-101) and two taskspecific network branches, is built and trained using the Road-Conditions and Cityscapes datasets. We reveal that utilizing task-dependent homoscedastic uncertainty in the learning process improvesmulti-task model performance on both tasks. When performing task adaptation, using a small set of additional data labeled with semantic information, we gain considerable RSCC improvements on complex models. Furthermore, we demonstrate increased model generalizability in multi-task models, with up to 12% higher F1-score compared to single-task models.

Place, publisher, year, edition, pages
2019. , p. 50
Keywords [en]
Computer Vision, Deep Learning, Machine Learning, Convolutional Neural Networks, Classification, Semantic Segmentation
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-157403ISRN: LIU-IMT-TFK-A–19/570–SEOAI: oai:DiVA.org:liu-157403DiVA, id: diva2:1323233
External cooperation
NIRA Dynamics AB
Subject / course
Computer Engineering
Presentation
2019-06-03, Algoritmen, B-Huset, Campus Valla, Linköping, 10:15 (English)
Supervisors
Examiners
Available from: 2019-06-12 Created: 2019-06-11 Last updated: 2019-06-12Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • de-DE
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More languages
Output format
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