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Defected Object Detection Through One-Class Support Vector Machine Exploiting Deep Features.

Gabriele Tarantino

Defected Object Detection Through One-Class Support Vector Machine Exploiting Deep Features.

Rel. Guido Albertengo. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Elettronica (Electronic Engineering), 2020

Abstract:

In the context of Industry 4.0, a fundamental role is covered by the detection of defected or damaged components in industrial processing lines. A huge amount of methods and approaches have been designed during the last decades, in order to achieve better and better detecting performance. During last years, automated visual inspection systems based on image processing analysis and neural networks became really diffused and efficient and an impressive number of different approaches have been explored, particularly after that deep neural networks consolidated their extremely efficiencies into extract complex features from image data. In this field, one-class classification methods (i.e., methods that recur only to good components training data) are acquiring more and more interest due to the fact that in many cases very limited data is available for the defected products. In this work, a one-class classification approach based on Support Vector Machine which exploits deep neural networks for feature extraction is explored in relation to a real industrial production case, bringing the system to an embedded platform and showing so a Proof-of-Concept for a final on-field application.

Relatori: Guido Albertengo
Anno accademico: 2019/20
Tipo di pubblicazione: Elettronica
Numero di pagine: 100
Informazioni aggiuntive: Tesi secretata. Fulltext non presente
Soggetti:
Corso di laurea: Corso di laurea magistrale in Ingegneria Elettronica (Electronic Engineering)
Classe di laurea: Nuovo ordinamento > Laurea magistrale > LM-29 - INGEGNERIA ELETTRONICA
Aziende collaboratrici: SANTER Reply S.p.a.
URI: http://webthesis.biblio.polito.it/id/eprint/14436
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