Spatio-Temporal Deep Learning-Based Methods for Defect Detection: An Industrial Application Study Case

Spatio-Temporal Deep Learning-Based Methods for Defect Detection: An Industrial Application Study Case

Data-driven methods—particularly machine learning techniques—are expected to play a key role in the headway of Industry 4.0. One increasingly popular application in this context is when anomaly detection is employed to test manufactured goods in assembly lines. In this work, we compare supervised, s...

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Detalles Bibliográficos
Autores principales: Lucas A. da Silva, Eulanda M. dos Santos, Leo Araújo, Natalia S. Freire, Max Vasconcelos, Rafael Giusti, David Ferreira, Anderson S. Jesus, Agemilson Pimentel, Caio F. S. Cruz, Ruan J. S. Belem, André S. Costa, Osmar A. da Silva
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
machine learning
video anomaly detection
classification
pattern recognition
weakly supervised learning
Technology
T
Engineering (General). Civil engineering (General)
TA1-2040
Biology (General)
QH301-705.5
Physics
QC1-999
Chemistry
QD1-999
Acceso en línea:https://doaj.org/article/767bfa1dee5a46788481ad3eea69f2e1
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https://doaj.org/article/767bfa1dee5a46788481ad3eea69f2e1

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