A Review of Computer Vision Techniques in the Detection of Metal Failures
This paper considers and contrasts several computer vision techniques used to detect defects in metallic components during manufacturing or in service. Methodologies include statistical analysis, weighted entropy modification, Fourier transformations, neural networks, and deep learning. Such systems...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2021-01-01
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| Series: | MATEC Web of Conferences |
| Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2021/18/matecconf_iceaf2021_02021.pdf |
| _version_ | 1818957061750259712 |
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| author | Fitzgerald Deborah Fragoudakis Roselita |
| author_facet | Fitzgerald Deborah Fragoudakis Roselita |
| author_sort | Fitzgerald Deborah |
| collection | DOAJ |
| description | This paper considers and contrasts several computer vision techniques used to detect defects in metallic components during manufacturing or in service. Methodologies include statistical analysis, weighted entropy modification, Fourier transformations, neural networks, and deep learning. Such systems are used by manufacturers to perform non-destructive testing and inspection of components at high speeds [1]; providing better error detection than traditional human visual inspection, and lower costs [2]. This is a review of the computer vision system comparing different mathematical analysis in order to illustrate the strengths and weaknesses relative to the nature of the defect. It includes exemplar that histograms and statistical analysis operate best with significant contrast between the defect and background, that co-occurrence matrix and Gabor filtering are computationally expensive, that structural analysis is useful when there are repeated patterns, that Fourier transforms, applied to spatial data, need windowing to capture localized issues, and that neural networks can be utilized after training. |
| first_indexed | 2024-12-20T11:03:52Z |
| format | Article |
| id | doaj.art-cc29d016fa294ae6aa0f135dab1673b5 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-12-20T11:03:52Z |
| publishDate | 2021-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-cc29d016fa294ae6aa0f135dab1673b52022-12-21T19:42:56ZengEDP SciencesMATEC Web of Conferences2261-236X2021-01-013490202110.1051/matecconf/202134902021matecconf_iceaf2021_02021A Review of Computer Vision Techniques in the Detection of Metal FailuresFitzgerald Deborah0Fragoudakis Roselita1Department of Mechanical Engineering, Merrimack College, North AndoverDepartment of Mechanical Engineering, Merrimack College, North AndoverThis paper considers and contrasts several computer vision techniques used to detect defects in metallic components during manufacturing or in service. Methodologies include statistical analysis, weighted entropy modification, Fourier transformations, neural networks, and deep learning. Such systems are used by manufacturers to perform non-destructive testing and inspection of components at high speeds [1]; providing better error detection than traditional human visual inspection, and lower costs [2]. This is a review of the computer vision system comparing different mathematical analysis in order to illustrate the strengths and weaknesses relative to the nature of the defect. It includes exemplar that histograms and statistical analysis operate best with significant contrast between the defect and background, that co-occurrence matrix and Gabor filtering are computationally expensive, that structural analysis is useful when there are repeated patterns, that Fourier transforms, applied to spatial data, need windowing to capture localized issues, and that neural networks can be utilized after training.https://www.matec-conferences.org/articles/matecconf/pdf/2021/18/matecconf_iceaf2021_02021.pdf |
| spellingShingle | Fitzgerald Deborah Fragoudakis Roselita A Review of Computer Vision Techniques in the Detection of Metal Failures MATEC Web of Conferences |
| title | A Review of Computer Vision Techniques in the Detection of Metal Failures |
| title_full | A Review of Computer Vision Techniques in the Detection of Metal Failures |
| title_fullStr | A Review of Computer Vision Techniques in the Detection of Metal Failures |
| title_full_unstemmed | A Review of Computer Vision Techniques in the Detection of Metal Failures |
| title_short | A Review of Computer Vision Techniques in the Detection of Metal Failures |
| title_sort | review of computer vision techniques in the detection of metal failures |
| url | https://www.matec-conferences.org/articles/matecconf/pdf/2021/18/matecconf_iceaf2021_02021.pdf |
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