CTIMS: Automated Defect Detection Framework Using Computed Tomography

Applied Sciences

Abderrazak Chahid¹  Hossam A. Gabbar¹  Md. Jamiul Alam Khan²  Oluwabukola Grace Adegboro²  Matthew Immanuel Samson³ 

[1] Faculty of Energy Systems and Nuclear Science, Ontario Tech University (UOIT), Oshawa, ON L1G 0C5, Canada;Faculty of Engineering and Applied Science, Ontario Tech University (UOIT), Oshawa, ON L1G 0C5, Canada;New Vision Systems Canada Inc. (NVS), Scarborough, ON M1S 3L1, Canada;

关键词: computerized tomography (CT);    defect inspection;    computer vision;    image processing;    deep learning;    toolbox;   

DOI  :  10.3390/app12042175

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown