R-CNN-Based Large-Scale Object-Defect Inspection System for Laser Cutting in the Automotive Industry

A car side-outer is an iron mold that is applied in the design and safety of the side of a vehicle, and is subjected to a complicated and detailed molding process. The side-outer has three features that make its quality inspection difficult to automate: (1) it is large; (2) there are many objects to...

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Autores principales:	Donggyun Im, Jongpil Jeong
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial intelligence laser cutting automotive industry defect inspection R-CNN Chemical technology TP1-1185 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/d1c9a63b9c3b470d9f2c43c1e31bf99c
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id	oai:doaj.org-article:d1c9a63b9c3b470d9f2c43c1e31bf99c
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spelling	oai:doaj.org-article:d1c9a63b9c3b470d9f2c43c1e31bf99c2021-11-25T18:51:43ZR-CNN-Based Large-Scale Object-Defect Inspection System for Laser Cutting in the Automotive Industry10.3390/pr91120432227-9717https://doaj.org/article/d1c9a63b9c3b470d9f2c43c1e31bf99c2021-11-01T00:00:00Zhttps://www.mdpi.com/2227-9717/9/11/2043https://doaj.org/toc/2227-9717A car side-outer is an iron mold that is applied in the design and safety of the side of a vehicle, and is subjected to a complicated and detailed molding process. The side-outer has three features that make its quality inspection difficult to automate: (1) it is large; (2) there are many objects to inspect; and (3) it must fulfil high-quality requirements. Given these characteristics, the industrial vision system for the side-outer is nearly impossible to apply, and indeed there is no reference for an automated defect-inspection system for the side-outer. Manual inspection of the side-outer worsens the quality and cost competitiveness of the metal-cutting companies. To address these problems, we propose a large-scale Object-Defect Inspection System based on Regional Convolutional Neural Network (R-CNN; RODIS) using Artificial Intelligence (AI) technology. In this paper, we introduce the framework, including the hardware composition and the inspection method of RODIS. We mainly focus on creating the proper dataset on-site, which should be prepared for data analysis and model development. Additionally, we share the trial-and-error experiences gained from the actual installation of RODIS on-site. We explored and compared various R-CNN backbone networks for object detection using actual data provided by a laser-cutting company. The Mask R-CNN models using Res-net-50-FPN show Average Precision (AP) of 71.63 (Object Detection) and 86.21 (Object Seg-mentation), which indicates a better performance than that of other models.Donggyun ImJongpil JeongMDPI AGarticleartificial intelligencelaser cuttingautomotive industrydefect inspectionR-CNNChemical technologyTP1-1185ChemistryQD1-999ENProcesses, Vol 9, Iss 2043, p 2043 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence laser cutting automotive industry defect inspection R-CNN Chemical technology TP1-1185 Chemistry QD1-999
spellingShingle	artificial intelligence laser cutting automotive industry defect inspection R-CNN Chemical technology TP1-1185 Chemistry QD1-999 Donggyun Im Jongpil Jeong R-CNN-Based Large-Scale Object-Defect Inspection System for Laser Cutting in the Automotive Industry
description	A car side-outer is an iron mold that is applied in the design and safety of the side of a vehicle, and is subjected to a complicated and detailed molding process. The side-outer has three features that make its quality inspection difficult to automate: (1) it is large; (2) there are many objects to inspect; and (3) it must fulfil high-quality requirements. Given these characteristics, the industrial vision system for the side-outer is nearly impossible to apply, and indeed there is no reference for an automated defect-inspection system for the side-outer. Manual inspection of the side-outer worsens the quality and cost competitiveness of the metal-cutting companies. To address these problems, we propose a large-scale Object-Defect Inspection System based on Regional Convolutional Neural Network (R-CNN; RODIS) using Artificial Intelligence (AI) technology. In this paper, we introduce the framework, including the hardware composition and the inspection method of RODIS. We mainly focus on creating the proper dataset on-site, which should be prepared for data analysis and model development. Additionally, we share the trial-and-error experiences gained from the actual installation of RODIS on-site. We explored and compared various R-CNN backbone networks for object detection using actual data provided by a laser-cutting company. The Mask R-CNN models using Res-net-50-FPN show Average Precision (AP) of 71.63 (Object Detection) and 86.21 (Object Seg-mentation), which indicates a better performance than that of other models.
format	article
author	Donggyun Im Jongpil Jeong
author_facet	Donggyun Im Jongpil Jeong
author_sort	Donggyun Im
title	R-CNN-Based Large-Scale Object-Defect Inspection System for Laser Cutting in the Automotive Industry
title_short	R-CNN-Based Large-Scale Object-Defect Inspection System for Laser Cutting in the Automotive Industry
title_full	R-CNN-Based Large-Scale Object-Defect Inspection System for Laser Cutting in the Automotive Industry
title_fullStr	R-CNN-Based Large-Scale Object-Defect Inspection System for Laser Cutting in the Automotive Industry
title_full_unstemmed	R-CNN-Based Large-Scale Object-Defect Inspection System for Laser Cutting in the Automotive Industry
title_sort	r-cnn-based large-scale object-defect inspection system for laser cutting in the automotive industry
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d1c9a63b9c3b470d9f2c43c1e31bf99c
work_keys_str_mv	AT donggyunim rcnnbasedlargescaleobjectdefectinspectionsystemforlasercuttingintheautomotiveindustry AT jongpiljeong rcnnbasedlargescaleobjectdefectinspectionsystemforlasercuttingintheautomotiveindustry
_version_	1718410620589572096

R-CNN-Based Large-Scale Object-Defect Inspection System for Laser Cutting in the Automotive Industry

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