A Deep Learning Approach to Detection and Classification of Small Defects on Painted Surfaces : A Study Made on Volvo GTO, Umeå

A Deep Learning Approach to Detection and Classification of Small Defects on Painted Surfaces : A Study Made on Volvo GTO, Umeå

In this thesis we conclude that convolutional neural networks, together with phase-measuring deflectometry techniques, can be used to create models which can detect and classify defects on painted surfaces very well, even compared to experienced humans. Further, we show which preprocessing measures...

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Bibliographic Details
Main Authors: Rönnqvist, Johannes, Sjölund, Johannes
Format: Others
Language:English
Published: Umeå universitet, Institutionen för matematik och matematisk statistik 2019
Subjects:
Deep Learning
Image Recognition
Classification
Defect Detection
Automation
Neural Networks
Convolutional Neural Networks
Phase-measuring Deflectometry
Mathematics
Matematik
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160194

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160194

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