CTIMS: Automated Defect Detection Framework Using Computed Tomography
Non-Destructive Testing (NDT) is one of the inspection techniques used in industrial tool inspection for quality and safety control. It is performed mainly using X-ray Computed Tomography (CT) to scan the internal structure of the tools and detect the potential defects. In this paper, we propose a n...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/4/2175 |
| _version_ | 1797482961213325312 |
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| author | Hossam A. Gabbar Abderrazak Chahid Md. Jamiul Alam Khan Oluwabukola Grace Adegboro Matthew Immanuel Samson |
| author_facet | Hossam A. Gabbar Abderrazak Chahid Md. Jamiul Alam Khan Oluwabukola Grace Adegboro Matthew Immanuel Samson |
| author_sort | Hossam A. Gabbar |
| collection | DOAJ |
| description | Non-Destructive Testing (NDT) is one of the inspection techniques used in industrial tool inspection for quality and safety control. It is performed mainly using X-ray Computed Tomography (CT) to scan the internal structure of the tools and detect the potential defects. In this paper, we propose a new toolbox called the CT-Based Integrity Monitoring System (CTIMS-Toolbox) for automated inspection of CT images and volumes. It contains three main modules: first, the database management module, which handles the database and reads/writes queries to retrieve or save the CT data; second, the pre-processing module for registration and background subtraction; third, the defect inspection module to detect all the potential defects (missing parts, damaged screws, etc.) based on a hybrid system composed of computer vision and deep learning techniques. This paper explores the different features of the CTIMS-Toolbox, exposes the performance of its modules, compares its features to some existing CT inspection toolboxes, and provides some examples of the obtained results. |
| first_indexed | 2024-03-09T22:41:00Z |
| format | Article |
| id | doaj.art-ce01049e452844d9a616ca9fa6b757d5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:41:00Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ce01049e452844d9a616ca9fa6b757d52023-11-23T18:40:30ZengMDPI AGApplied Sciences2076-34172022-02-01124217510.3390/app12042175CTIMS: Automated Defect Detection Framework Using Computed TomographyHossam A. Gabbar0Abderrazak Chahid1Md. Jamiul Alam Khan2Oluwabukola Grace Adegboro3Matthew Immanuel Samson4Faculty of Energy Systems and Nuclear Science, Ontario Tech University (UOIT), Oshawa, ON L1G 0C5, CanadaFaculty of Energy Systems and Nuclear Science, Ontario Tech University (UOIT), Oshawa, ON L1G 0C5, CanadaFaculty of Engineering and Applied Science, Ontario Tech University (UOIT), Oshawa, ON L1G 0C5, CanadaFaculty of Engineering and Applied Science, Ontario Tech University (UOIT), Oshawa, ON L1G 0C5, CanadaNew Vision Systems Canada Inc. (NVS), Scarborough, ON M1S 3L1, CanadaNon-Destructive Testing (NDT) is one of the inspection techniques used in industrial tool inspection for quality and safety control. It is performed mainly using X-ray Computed Tomography (CT) to scan the internal structure of the tools and detect the potential defects. In this paper, we propose a new toolbox called the CT-Based Integrity Monitoring System (CTIMS-Toolbox) for automated inspection of CT images and volumes. It contains three main modules: first, the database management module, which handles the database and reads/writes queries to retrieve or save the CT data; second, the pre-processing module for registration and background subtraction; third, the defect inspection module to detect all the potential defects (missing parts, damaged screws, etc.) based on a hybrid system composed of computer vision and deep learning techniques. This paper explores the different features of the CTIMS-Toolbox, exposes the performance of its modules, compares its features to some existing CT inspection toolboxes, and provides some examples of the obtained results.https://www.mdpi.com/2076-3417/12/4/2175computerized tomography (CT)defect inspectioncomputer visionimage processingdeep learningtoolbox |
| spellingShingle | Hossam A. Gabbar Abderrazak Chahid Md. Jamiul Alam Khan Oluwabukola Grace Adegboro Matthew Immanuel Samson CTIMS: Automated Defect Detection Framework Using Computed Tomography Applied Sciences computerized tomography (CT) defect inspection computer vision image processing deep learning toolbox |
| title | CTIMS: Automated Defect Detection Framework Using Computed Tomography |
| title_full | CTIMS: Automated Defect Detection Framework Using Computed Tomography |
| title_fullStr | CTIMS: Automated Defect Detection Framework Using Computed Tomography |
| title_full_unstemmed | CTIMS: Automated Defect Detection Framework Using Computed Tomography |
| title_short | CTIMS: Automated Defect Detection Framework Using Computed Tomography |
| title_sort | ctims automated defect detection framework using computed tomography |
| topic | computerized tomography (CT) defect inspection computer vision image processing deep learning toolbox |
| url | https://www.mdpi.com/2076-3417/12/4/2175 |
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