Deep learning-supported machine vision-based hybrid system combining inhomogeneous 2D and 3D data for the identification of surface defects
Machine vision systems for automatic defect detection commonly adopt 2D image-based systems or 3D laser triangulation systems. 2D and 3D systems present opposite advantages and disadvantages depending on the typology and position of defects to be detected. When the variety of defects is large, none...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Production and Manufacturing Research: An Open Access Journal |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21693277.2024.2378199 |
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| author | Giorgio Cavaliere Oswald Lanz Yuri Borgianni Enrico Savio |
| author_facet | Giorgio Cavaliere Oswald Lanz Yuri Borgianni Enrico Savio |
| author_sort | Giorgio Cavaliere |
| collection | DOAJ |
| description | Machine vision systems for automatic defect detection commonly adopt 2D image-based systems or 3D laser triangulation systems. 2D and 3D systems present opposite advantages and disadvantages depending on the typology and position of defects to be detected. When the variety of defects is large, none of them performs defect detection accurately. To overcome this limitation, this paper illustrates a hybrid Deep Learning-supported system where the 2D- and 3D-generated data are juxtaposed and analyzed contextually. Anomaly scores are subsequently determined to distinguish suitable and uncompliant parts. The implementation of the hybrid system allowed the identification of defective parts in an aluminium die-cast component with an accuracy concerning true positives of over 95% by comparing the system outputs with human defect detection. The inspection time was reduced by approximately 20% if compared, once again, with the same activities performed by humans. |
| format | Article |
| id | doaj-art-958dfa97134b4c4d832ea01b3fa3be49 |
| institution | Kabale University |
| issn | 2169-3277 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Production and Manufacturing Research: An Open Access Journal |
| spelling | doaj-art-958dfa97134b4c4d832ea01b3fa3be492024-12-09T15:50:02ZengTaylor & Francis GroupProduction and Manufacturing Research: An Open Access Journal2169-32772024-12-0112110.1080/21693277.2024.2378199Deep learning-supported machine vision-based hybrid system combining inhomogeneous 2D and 3D data for the identification of surface defectsGiorgio Cavaliere0Oswald Lanz1Yuri Borgianni2Enrico Savio3Faculty of Engineering, Free University of Bozen-Bolzano, Piazza Università, Bolzano (BZ), ItalyFaculty of Engineering, Free University of Bozen-Bolzano, Piazza Università, Bolzano (BZ), ItalyFaculty of Engineering, Free University of Bozen-Bolzano, Piazza Università, Bolzano (BZ), ItalyDepartment of Industrial Engineering, University of Padua, Padua (PD), ItalyMachine vision systems for automatic defect detection commonly adopt 2D image-based systems or 3D laser triangulation systems. 2D and 3D systems present opposite advantages and disadvantages depending on the typology and position of defects to be detected. When the variety of defects is large, none of them performs defect detection accurately. To overcome this limitation, this paper illustrates a hybrid Deep Learning-supported system where the 2D- and 3D-generated data are juxtaposed and analyzed contextually. Anomaly scores are subsequently determined to distinguish suitable and uncompliant parts. The implementation of the hybrid system allowed the identification of defective parts in an aluminium die-cast component with an accuracy concerning true positives of over 95% by comparing the system outputs with human defect detection. The inspection time was reduced by approximately 20% if compared, once again, with the same activities performed by humans.https://www.tandfonline.com/doi/10.1080/21693277.2024.2378199Surface defects analysishybrid 2D and 3D systemmachine vision systemsdeep learninghigh-pressure die-casting |
| spellingShingle | Giorgio Cavaliere Oswald Lanz Yuri Borgianni Enrico Savio Deep learning-supported machine vision-based hybrid system combining inhomogeneous 2D and 3D data for the identification of surface defects Production and Manufacturing Research: An Open Access Journal Surface defects analysis hybrid 2D and 3D system machine vision systems deep learning high-pressure die-casting |
| title | Deep learning-supported machine vision-based hybrid system combining inhomogeneous 2D and 3D data for the identification of surface defects |
| title_full | Deep learning-supported machine vision-based hybrid system combining inhomogeneous 2D and 3D data for the identification of surface defects |
| title_fullStr | Deep learning-supported machine vision-based hybrid system combining inhomogeneous 2D and 3D data for the identification of surface defects |
| title_full_unstemmed | Deep learning-supported machine vision-based hybrid system combining inhomogeneous 2D and 3D data for the identification of surface defects |
| title_short | Deep learning-supported machine vision-based hybrid system combining inhomogeneous 2D and 3D data for the identification of surface defects |
| title_sort | deep learning supported machine vision based hybrid system combining inhomogeneous 2d and 3d data for the identification of surface defects |
| topic | Surface defects analysis hybrid 2D and 3D system machine vision systems deep learning high-pressure die-casting |
| url | https://www.tandfonline.com/doi/10.1080/21693277.2024.2378199 |
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