[논문]부식 검출과 분석에 적용한 영상 처리 기술 동향

김범수; 권재성; 양정현

doi:10.5695/jsse.2023.56.6.353

[국내논문] 부식 검출과 분석에 적용한 영상 처리 기술 동향
Trends in image processing techniques applied to corrosion detection and analysis 원문보기

한국표면공학회지 = Journal of the Korean institute of surface engineering, v.56 no.6, 2023년, pp.353 - 370

김범수 (경상국립대학교 기계시스템공학과) , 권재성 (경상국립대학교 기계시스템공학과) , 양정현 (경상국립대학교 기계시스템공학과)

Abstract ▼ AI-Helper

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.

주제어

표/그림 (17)

그림 Fig. 1. Color models (a) RGB model (b) HSV model (c) CIELab model
그림 Fig. 2. Corrosion detection and analysis process using image processing.
그림 Fig. 3. Grabcut algorithm (a) Input image, (b) Grabcut segmentation process
그림 Fig. 4. support vector machine (a) input data and hyperplanes, (b) support vectors lie on the marginal hyperplane, (c) new input data and classification
그림 Fig. 5. Supervised machine learning process
그림 Fig. 6. Neural network structure with two hidden layers
그림 Fig. 7. ANN
그림 Fig. 8. A generic CNN Architecture
그림 Fig. 9. Convolution process
그림 Fig. 10. R-CNN architecture
그림 Fig. 11. Faster R-CNN structure [36]
그림 Fig. 12. HED architecture. side output from the conv1_2, conv2_2, conv3_3, conv_4_3, conv5_3 layers
그림 Fig. 13. (a) Sample specimen, rust with probability 1.0, (b) Apple(source: pixabay), no rust with probability 0.22, (c) Sunflower(source: pixabay), no rust with probability 0.42
그림 fig.14. (a) Sample specimen (b) K-means with K=5 (c) Fuzzy C-means with k=5 (d) DBSCAN with є=5, (e) HED output(edge) (f) HED segment using blob(binary large object) 364
그림 Fig. 15. Hot-dip Galvanized Iron(HGI) (a) 30 day, (b) 180 day, (c) 360 day, (d) trend of average 'a' values.
그림 Fig. 16. 30 days HGI (a) a vs. b, (b) a vs. L, (c) 3D scatter plot
그림 Fig. 17. 360 days HGI (a) a vs. b, (b) a vs. L, (c) 3D scatter plot

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저자의 다른 논문 :

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용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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