광학 밀도 기반 이미지 정규화를 이용한 장기간 부식 시편 이미지 분석
Long-term Corrosion Specimen Image Analysis Using Optical Density-Based Image Normalization
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Corrosion of metals poses a threat to structural integrity in various industrial sectors and can intensify with prolonged exposure. This study proposes an efficient method for corrosion detection and analysis. A heuristic approach was used to derive a corrosion matrix composed of corrosion area colors and specimen surface colors, utilizing the CIEDE2000 color difference criterion. By converting specimen images into optical density space and performing color normalization, consistency in color changes was maintained during long-term observation. The combination of Optical Density transformation and HSV color space transformation provides an effective and consistent method for analyzing the corrosion process. This approach is expected to enhance the performance of corrosion monitoring systems and improve structural safety.
Abstract
Corrosion of metals poses a threat to structural integrity in various industrial sectors and can intensify with prolonged exposure. This study proposes an efficient method for corrosion detection and analysis. A heuristic approach was used to derive a corrosion matrix composed of corrosion area colors and specimen surface colors, utilizing the CIEDE2000 color difference criterion. By converting specimen images into optical density space and performing color normalization, consistency in color changes was maintained during long-term observation. The combination of Optical Density transformation and HSV color space transformation provides an effective and consistent method for analyzing the corrosion process. This approach is expected to enhance the performance of corrosion monitoring systems and improve structural safety.
참고문헌 (17)
- K. W. Liao, "Detection of rust defects on steel bridge coatings via digital image recognition", Automation in Construction, 71, pp.294~306 (2016)
- M. S. Bhargava, "Sensors in advancing the capabilities of corrosion detection : A review", Sensors and Actuators A : Physical, 332, pp.113086~ (2021)
- H. Zhu, "Mechanical impedance-based technique for steel structural corrosion damage detection", Measurement, 88, pp.353~359 (2016)
- S. Doshvarpassand, "An overview of corrosion defect characterization using active infrared thermography", Infrared Physics & Technology, 96, pp.366~389 (2019)
- M. Enikeev, "Analysis of corrosion process development on metals by means of computer vision", Engineering Journal, 21, pp.183~192 (2017)
- B. Kim, "Trends in image processing techniques applied to corrosion detection and analysis", Journal of Surface Science and Engineering, 56, pp.353~370 (2023)
- X. Lv, "Deep metallic surface defect detection : The new benchmark and detection network", Sensors, 20, pp.1562~ (2020)
- R.C. Gonzales, "Digital image processing", pp.133~152 (2018), Pearson
- J. Xiong, "Application of histogram equalization for image enhancement in corrosion areas", Shock and Vibration, 1, pp.8883571~ (2021)
- U. Manikpuri, "Image enhancement through logarithmic transformation", International Journal InnovativeAdvances in engineering research, 1, pp.357~362 (2014)
- S. Roy, "A study about color normalization methods for histopathology images", Micron, 114, pp.42~61 (2018)
- P. H. Chen, "Automated bridge coating defect recognition using adaptive ellipse approach", Automation in Construction, 18, pp.632~643 (2009)
- M. Macenko, "A method for normalizing histology slides for quantitative analysis", 2009 IEEE International Symposium on Biomedical Imaging, pp.1107~1110 (2009), IEEE
- N. Alsubaie, "Stain deconvolution using statistical analysis of multi-resolution stain colour representation", PloS One, 12, pp.e0169875~ (2017)
- A. M. Khan, "A nonlinear mapping approach to stain normalization in digital histopathology images using image-specific color deconvolution", IEEE Transactions on Biomedical Engineering, 61, pp.1729~1736 (2014)
- A. Pereira, "Efficient CIEDE2000-based color similarity decision for computer vision", IEEE Transactions on Circuits and Systems for Video Technology, 30, pp.2141~2154 (2019)
- P. Berens, "CircStat: a MATLAB toolbox for circular statistics", Journal of Statistical Software, 31, pp.1~21 (2009)