부식 검출과 분석에 적용한 영상 처리 기술 동향Trends in image processing techniques applied to corrosion detection and analysis
- Other Titles
- Trends in image processing techniques applied to corrosion detection and analysis
- Authors
- 김범수; 권재성; 양정현
- Issue Date
- Dec-2023
- Publisher
- 한국표면공학회
- Keywords
- Corrosion; Color Models; Image Segmentation; Machine Learning
- Citation
- 한국표면공학회지, v.56, no.6, pp 353 - 370
- Pages
- 18
- Indexed
- KCI
- Journal Title
- 한국표면공학회지
- Volume
- 56
- Number
- 6
- Start Page
- 353
- End Page
- 370
- URI
- https://scholarworks.gnu.ac.kr/handle/sw.gnu/69261
- ISSN
- 1225-8024
2288-8403
- Abstract
- Corrosion detection and analysis is a very important topic in reducing costs and preventing disasters. Recently, image processing techniques have been widely applied to corrosion identification and analysis. In this work, we briefly introduces traditional image processing techniques and machine learning algorithms applied to detect or analyze corrosion in various fields. Recently, machine learning, especially CNN-based algorithms, have been widely applied to corrosion detection. Additionally, research on applying machine learning to region segmentation is very actively underway. The corrosion is reddish and brown in color and has a very irregular shape, so a combination of techniques that consider color and texture, various mathematical techniques, and machine learning algorithms are used to detect and analyze corrosion. We present examples of the application of traditional image processing techniques and machine learning to corrosion detection and analysis.
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