GS&E journal > Semantic Segmentation with Deep Learning: Detection of Cracks at the Cut Edge of Glass

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, th...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Michael Drass, Hagen Berthold, Michael Anton Kraus, Steffen Müller-Braun
Formato:	article
Lenguaje:	EN
Publicado:	Challenging Glass Conference 2020
Materias:	Clay industries. Ceramics. Glass TP785-869
Acceso en línea:	https://doaj.org/article/cb862a95738f4aefbd6b869857d959ff
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:cb862a95738f4aefbd6b869857d959ff
record_format	dspace
spelling	oai:doaj.org-article:cb862a95738f4aefbd6b869857d959ff2021-12-05T05:13:10ZGS&E journal > Semantic Segmentation with Deep Learning: Detection of Cracks at the Cut Edge of Glass2589-8019https://doaj.org/article/cb862a95738f4aefbd6b869857d959ff2020-09-01T00:00:00Zhttps://proceedings.challengingglass.com/index.php/cgc/article/view/264https://doaj.org/toc/2589-8019 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. Michael DrassHagen BertholdMichael Anton KrausSteffen Müller-BraunChallenging Glass ConferencearticleClay industries. Ceramics. GlassTP785-869ENChallenging Glass Conference Proceedings, Vol 7, Iss 1 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Clay industries. Ceramics. Glass TP785-869
spellingShingle	Clay industries. Ceramics. Glass TP785-869 Michael Drass Hagen Berthold Michael Anton Kraus Steffen Müller-Braun GS&E journal > Semantic Segmentation with Deep Learning: Detection of Cracks at the Cut Edge of Glass
description	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.
format	article
author	Michael Drass Hagen Berthold Michael Anton Kraus Steffen Müller-Braun
author_facet	Michael Drass Hagen Berthold Michael Anton Kraus Steffen Müller-Braun
author_sort	Michael Drass
title	GS&E journal > Semantic Segmentation with Deep Learning: Detection of Cracks at the Cut Edge of Glass
title_short	GS&E journal > Semantic Segmentation with Deep Learning: Detection of Cracks at the Cut Edge of Glass
title_full	GS&E journal > Semantic Segmentation with Deep Learning: Detection of Cracks at the Cut Edge of Glass
title_fullStr	GS&E journal > Semantic Segmentation with Deep Learning: Detection of Cracks at the Cut Edge of Glass
title_full_unstemmed	GS&E journal > Semantic Segmentation with Deep Learning: Detection of Cracks at the Cut Edge of Glass
title_sort	gs&e journal > semantic segmentation with deep learning: detection of cracks at the cut edge of glass
publisher	Challenging Glass Conference
publishDate	2020
url	https://doaj.org/article/cb862a95738f4aefbd6b869857d959ff
work_keys_str_mv	AT michaeldrass gsejournalsemanticsegmentationwithdeeplearningdetectionofcracksatthecutedgeofglass AT hagenberthold gsejournalsemanticsegmentationwithdeeplearningdetectionofcracksatthecutedgeofglass AT michaelantonkraus gsejournalsemanticsegmentationwithdeeplearningdetectionofcracksatthecutedgeofglass AT steffenmullerbraun gsejournalsemanticsegmentationwithdeeplearningdetectionofcracksatthecutedgeofglass
_version_	1718372642047655936

GS&E journal > Semantic Segmentation with Deep Learning: Detection of Cracks at the Cut Edge of Glass

Ejemplares similares