Self-supervised pre-training of CNNs for flatness defect classification in the steelworks industry

International Journal of Advances in Intelligent Informatics

View Publication Info
Field Value
Title Self-supervised pre-training of CNNs for flatness defect classification in the steelworks industry
Creator Galli, Filippo
Ritacco, Antonio
Lanciano, Giacomo
Vannocci, Marco
Colla, Valentina
Vannucci, Marco
Subject Self-supervision; Steelworks; Deep learning; CNN
Description Classification of surface defects in the steelworks industry plays a significant role in guaranteeing the quality of the products. From an industrial point of view, a serious concern is represented by the hot-rolled products shape defects and particularly those concerning the strip flatness. Flatness defects are typically divided into four sub-classes depending on which part of the strip is affected and the corresponding shape. In the context of this research, the primary objective is evaluating the improvements of exploiting the self-supervised learning paradigm for defects classification, taking advantage of unlabelled, real, steel strip flatness maps. Different pre-training methods are compared, as well as architectures, taking advantage of well-established neural subnetworks, such as Residual and Inception modules. A systematic approach in evaluating the different performances guarantees a formal verification of the self-supervised pre-training paradigms evaluated hereafter. In particular, pre-training neural networks with the EgoMotion meta-algorithm shows classification improvements over the AutoEncoder technique, which in turn is better performing than a Glorot weight initialization.
Publisher Universitas Ahmad Dahlan
Date 2020-03-29
Type info:eu-repo/semantics/article

Format application/pdf
Source International Journal of Advances in Intelligent Informatics; Vol 6, No 1 (2020): March 2020; 13-22
Language eng

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us


Copyright © 2015-2018 Simon Fraser University Library