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All Journal International Journal of Advances in Intelligent Informatics
Valentina Colla
Scuola Superiore Sant’Anna, via Moruzzi 1
Computer Science & IT

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Self-supervised pre-training of CNNs for flatness defect classification in the steelworks industry Filippo Galli; Antonio Ritacco; Giacomo Lanciano; Marco Vannocci; Valentina Colla; Marco Vannucci
International Journal of Advances in Intelligent Informatics Vol 6, No 1 (2020): March 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v6i1.410

Abstract

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.
Co-Authors Antonio Ritacco Filippo Galli Giacomo Lanciano Marco Vannocci Marco Vannucci