Semantic segmentation with deep learning: detection of cracks at the cut edge of glass
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RESEARCH PAPER
Semantic segmentation with deep learning: detection of cracks at the cut edge of glass Michael Drass · Hagen Berthold · Michael A. Kraus · Steffen Müller-Braun
Received: 10 May 2020 / Accepted: 20 August 2020 © The Author(s) 2020
Abstract In this paper, artificial intelligence (AI) will be applied for the first time in the context of glass processing. The goal is to use an algorithm based on artificial intelligence to detect the fractured edge of a cut glass in order to generate a so-called mask image by AI. In the context of AI, this is a classical problem of semantic segmentation, in which objects (here the cut-edge of the cut glass) are automatically surrounded by the power of AI or detected and drawn. An original image of a cut glass edge is implemented into a deep neural net and processed in such a way that a mask image, i.e. an image of the cut edge, is automatically generated. Currently, this is only possible by manual tracing the cut-edge due to the fact that the crack contour of glass can sometimes only be recognized roughly. After manually marking the crack using an image processing program, the contour is then automatically evaluated further. AI and deep learning may provide the potential to automate the step of manual detection of the cut-edge of cut glass to great extent. In addition to the enormous time savings, the objectivity and reproducibility of detection is an important aspect, which will be addressed in this paper. M. Drass (B) · M. A. Kraus M&M Network-Ing UG (haftungsbeschränkt), Darmstadt, Germany e-mail: [email protected] e-mail: [email protected] M. Drass· H. Berthold · M. A. Kraus · S. Müller-Braun TU Darmstadt - Institute for Structural Mechanics and Design, Darmstadt, Germany
Keywords Deep learning · Semantic segmentation · Cut-edge glass · U-Net · UXception
1 Introduction 1.1 Cutting of glass In the production and further processing of annealed float glass, the glass panes are usually brought into the required dimensions by a cutting process. In a first step, a fissure is generated on the glass surface by using a cutting wheel. In the second step, the cut is opened along the fissure by applying a bending stress. This cutting process is influenced by many parameters (MüllerBraun et al. 2020). The edge strength in particular can be reproducibly increased by a proper adjustment of the parameters (Ensslen and Müller-Braun 2017). Furthermore, it could be observed that due to different cutting process parameters, the resulting damage to the edge (the crack system) can differ in its extent (Fig. 1). In addition, this characteristic of the crack system can be brought into a relationship with the strength (MüllerBraun et al. 2018). In particular, it has been found that when the edge is viewed perpendicular to the glass surface (Fig. 2), the so-called lateral cracks, which can be observed here, allow the best predictions for strength (Müller-Braun et al. 2020). The challenge here is to detect these lateral cracks in an accurate way. Currently, this is only pos
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