Image-Based Surface Defect Detection Using Deep Learning: A Review
| Autor: | Rishi K. Malhan, Yeo Jung Yoon, Prahar M. Bhatt, Pradeep Rajendran, Brual C. Shah, Satyandra K. Gupta, Shantanu Thakar |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Surface (mathematics)
0209 industrial biotechnology Artificial neural network business.industry Computer science Deep learning Pattern recognition 02 engineering and technology Computer Graphics and Computer-Aided Design Industrial and Manufacturing Engineering Computer Science Applications 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Software Image based |
| Zdroj: | Journal of Computing and Information Science in Engineering. 21 |
| ISSN: | 1944-7078 1530-9827 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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