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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
김범수권재성양정현
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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해양과학대학 > 기계시스템공학과 > Journal Articles

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해양과학대학 (기계시스템공학과)
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