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

(1) * Filippo Galli Mail (Scuola Superiore Sant’Anna, via Moruzzi 1, Italy)
(2) Antonio Ritacco Mail (Scuola Superiore Sant’Anna, via Moruzzi 1, Italy)
(3) Giacomo Lanciano Mail (Scuola Superiore Sant’Anna, via Moruzzi 1, Italy)
(4) Marco Vannocci Mail (Scuola Superiore Sant’Anna, via Moruzzi 1, Italy)
(5) Valentina Colla Mail (Scuola Superiore Sant’Anna, via Moruzzi 1, Italy)
(6) Marco Vannucci Mail (Scuola Superiore Sant’Anna, via Moruzzi 1, Italy)
*corresponding author

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.

Keywords


Self-supervision; Steelworks; Deep learning; CNN

   

DOI

https://doi.org/10.26555/ijain.v6i1.410
      

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