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A Deep Learning Approach to Detection and Classification of Small Defects on Painted Surfaces: A Study Made on Volvo GTO, Umeå
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. (FIQA)
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. (FIQA)
2019 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

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 enhances the performance of the models. We see that standardisation does increase the classification accuracy of the models. We demonstrate that cleaning the data through relabelling and removing faulty images improves classification accuracy and especially the models' ability to distinguish between different types of defects. We show that oversampling might be a feasible method to improve accuracy through increasing and balancing the data set by augmenting existing observations. Lastly, we find that combining many images with different patterns heavily increases the classification accuracy of the models. Our proposed approach is demonstrated to work well in a real-time factory environment. An automated quality control of the painted surfaces of Volvo Truck cabins could give great benefits in cost and quality. The automated quality control could provide data for a root-cause analysis and a quick and efficient alarm system. This could significantly streamline production and at the same time reduce costs and errors in production. Corrections and optimisation of the processes could be made in earlier stages in time and with higher precision than today.

Abstract [sv]

I den här rapporten visar vi att modeller av typen convolutional neural networks, tillsammans med phase-measuring deflektometri, kan hitta och klassificera defekter på målade ytor med hög precision, även jämfört med erfarna operatörer. Vidare visar vi vilka databehandlingsåtgärder som ökar modellernas prestanda. Vi ser att standardisering ökar modellernas klassificeringsförmåga. Vi visar att städning av data genom ommärkning och borttagning av felaktiga bilder förbättrar klassificeringsförmågan och särskilt modellernas förmåga att särskilja mellan olika typer av defekter. Vi visar att översampling kan vara en metod för att förbättra precisionen genom att öka och balansera datamängden genom att förändra och duplicera befintliga observationer. Slutligen finner vi att kombinera flera bilder med olika mönster ökar modellernas klassificeringsförmåga väsentligt. Vårt föreslagna tillvägagångssätt har visat sig fungera bra i realtid inom en produktionsmiljö. En automatiserad kvalitetskontroll av de målade ytorna på Volvos lastbilshytter kan ge stora fördelar med avseende på kostnad och kvalitet. Den automatiska kvalitetskontrollen kan ge data för en rotorsaksanalys och ett snabbt och effektivt alarmsystem. Detta kan väsentligt effektivisera produktionen och samtidigt minska kostnader och fel i produktionen. Korrigeringar och optimering av processerna kan göras i tidigare skeden och med högre precision än idag.

Place, publisher, year, edition, pages
2019. , p. 51
Keywords [en]
Deep Learning, Image Recognition, Classification, Defect Detection, Automation, Neural Networks, Convolutional Neural Networks, Phase-measuring Deflectometry
National Category
Mathematics
Identifiers
URN: urn:nbn:se:umu:diva-160194OAI: oai:DiVA.org:umu-160194DiVA, id: diva2:1325010
External cooperation
Volvo Group; Volvo Car Group
Educational program
Master of Science in Engineering and Management
Presentation
2019-06-05, N420, Johan Bures väg 14, Umeå, 13:15 (English)
Supervisors
Examiners
Available from: 2019-06-14 Created: 2019-06-14 Last updated: 2019-06-17Bibliographically approved

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