Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review
Over the last few decades, detecting surface defects has attracted significant attention as a challenging task. There are specific classes of problems that can be solved using traditional image processing techniques. However, these techniques struggle with complex textures in backgrounds, noise, and...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/16/2/95 |
| _version_ | 1827759134725898240 |
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| author | Alireza Saberironaghi Jing Ren Moustafa El-Gindy |
| author_facet | Alireza Saberironaghi Jing Ren Moustafa El-Gindy |
| author_sort | Alireza Saberironaghi |
| collection | DOAJ |
| description | Over the last few decades, detecting surface defects has attracted significant attention as a challenging task. There are specific classes of problems that can be solved using traditional image processing techniques. However, these techniques struggle with complex textures in backgrounds, noise, and differences in lighting conditions. As a solution to this problem, deep learning has recently emerged, motivated by two main factors: accessibility to computing power and the rapid digitization of society, which enables the creation of large databases of labeled samples. This review paper aims to briefly summarize and analyze the current state of research on detecting defects using machine learning methods. First, deep learning-based detection of surface defects on industrial products is discussed from three perspectives: supervised, semi-supervised, and unsupervised. Secondly, the current research status of deep learning defect detection methods for X-ray images is discussed. Finally, we summarize the most common challenges and their potential solutions in surface defect detection, such as unbalanced sample identification, limited sample size, and real-time processing. |
| first_indexed | 2024-03-11T09:15:49Z |
| format | Article |
| id | doaj.art-38c2602459e4485baa4708bd24a9eee4 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-11T09:15:49Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-38c2602459e4485baa4708bd24a9eee42023-11-16T18:37:41ZengMDPI AGAlgorithms1999-48932023-02-011629510.3390/a16020095Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A ReviewAlireza Saberironaghi0Jing Ren1Moustafa El-Gindy2Department of Electrical, Computer and Software Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, CanadaDepartment of Electrical, Computer and Software Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, CanadaDepartment of Automotive and Mechatronics Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, CanadaOver the last few decades, detecting surface defects has attracted significant attention as a challenging task. There are specific classes of problems that can be solved using traditional image processing techniques. However, these techniques struggle with complex textures in backgrounds, noise, and differences in lighting conditions. As a solution to this problem, deep learning has recently emerged, motivated by two main factors: accessibility to computing power and the rapid digitization of society, which enables the creation of large databases of labeled samples. This review paper aims to briefly summarize and analyze the current state of research on detecting defects using machine learning methods. First, deep learning-based detection of surface defects on industrial products is discussed from three perspectives: supervised, semi-supervised, and unsupervised. Secondly, the current research status of deep learning defect detection methods for X-ray images is discussed. Finally, we summarize the most common challenges and their potential solutions in surface defect detection, such as unbalanced sample identification, limited sample size, and real-time processing.https://www.mdpi.com/1999-4893/16/2/95defect detectionsurface defect detectiondefect detection for X-ray imagesdefect recognitiondeep learning |
| spellingShingle | Alireza Saberironaghi Jing Ren Moustafa El-Gindy Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review Algorithms defect detection surface defect detection defect detection for X-ray images defect recognition deep learning |
| title | Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review |
| title_full | Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review |
| title_fullStr | Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review |
| title_full_unstemmed | Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review |
| title_short | Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review |
| title_sort | defect detection methods for industrial products using deep learning techniques a review |
| topic | defect detection surface defect detection defect detection for X-ray images defect recognition deep learning |
| url | https://www.mdpi.com/1999-4893/16/2/95 |
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