부식 검출과 분석에 적용한 영상 처리 기술 동향

• Published in: Biuletyn Uniejowski Vol. 56; no. 6; pp. 353 - 370
• Main Authors: 김범수(Beomsoo Kim), 권재성(Jaesung Kwon), 양정현(Jeonghyeon Yang)
• Format: Journal Article
• Language: Korean
• Published: 한국표면공학회 2023
• Subjects: 기계공학; Color Models; Corrosion; Machine Learning; Image Segmentation
• ISSN: 1225-8024; 2299-8403; 2288-8403
• DOI: 10.5695/JSSE.2023.56.6.353

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.