Name
Abstract | ||
|---|---|---|
Automatically detecting surface defects from images is an essential capability in manufacturing applications. Traditional image processing techniques are useful in solving a specific class of problems. However, these techniques do not handle noise, variations in lighting conditions, and backgrounds with complex textures. In recent times, deep learning has been widely explored for use in automation of defect detection. This survey article presents three different ways of classifying various efforts in literature for surface defect detection using deep learning techniques. These three ways are based on defect detection context, learning techniques, and defect localization and classification method respectively. This article also identifies future research directions based on the trends in the deep learning area. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1115/1.4049535 | JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING |
Keywords | DocType | Volume |
deep learning, inspection, defect detection, image processing, machine learning, computer-aided manufacturing, automation, artificial intelligence | Journal | 21 |
Issue | ISSN | Citations |
4 | 1530-9827 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Prahar M. Bhatt | 1 | 3 | 2.50 |
| Rishi K. Malhan | 2 | 2 | 2.79 |
| Pradeep Rajendran | 3 | 0 | 0.34 |
| Brual C. Shah | 4 | 15 | 4.85 |
| Shantanu Thakar | 5 | 2 | 2.91 |
| Yeo Jung Yoon | 6 | 0 | 1.01 |
| Satyandra K Gupta | 7 | 687 | 77.11 |