Evaluation of Red Rust Area Ratio and Rating Number for Greenhouse Saddle Clamps Based on HSV-RGB Image Analysis

Jaehun Lee; Junghwa Park; Eunji Jung; Gunhui Park; Sungwook Yun; Jaesung Park

doi:10.12791/KSBEC.2026.35.3.241

All Issue

2026 Vol.35, Issue 3 Preview Page Next Page

Original Articles

Evaluation of Red Rust Area Ratio and Rating Number for Greenhouse Saddle Clamps Based on HSV-RGB Image Analysis HSV-RGB 영상 분석 기반 온실용 새들 클램프의 적청 면적률 및 RN 등급 평가

31 July 2026. pp. 241-255

PDF XML

Abstract

Agricultural greenhouse structures are continuously exposed to high-temperature and high-humidity environments, which accelerates the corrosion of metal fastening components and significantly compromises structural safety. Conventional visual inspection methods for corrosion assessment are limited by their lack of objectivity, as they are susceptible to observer subjectivity as well as environmental factors such as lighting conditions. To address these limitations, this study proposes an automated, image processing-based algorithm for the quantitative evaluation of red rust grades on saddle clamps, which are critical connecting components in greenhouses. To analyze accelerated corrosion behavior, specimens were obtained by conducting neutral salt spray (NSS) tests for durations ranging from 24 to 120 hours, digital images were subsequently acquired under controlled lighting and photographic conditions. To eliminate background noise and prevent the false detection of initial white rust, preprocessing and a combined HSV-RGB filtering technique were applied, enabling the effective extraction of actual red rust areas. Validation against the KS D 9502 Rating Number (RN) standard images demonstrated a high numerical consistency, thereby indicating the potential of the proposed algorithm as a quantitative evaluation tool. The results of this study are expected to serve as foundational data for the development of automated corrosion monitoring and systematic maintenance systems for greenhouse structures in the future.

Keywords

corrosion area ratio

HSV-RGB image analysis

rating number

saddle clamp

salt spray test

농업용 온실 구조물은 고온·다습한 환경에 노출되어 금속 체결 부품의 부식이 빠르게 진행되며, 이는 구조적 안정성을 저하시키는 주요 원인이 된다. 기존의 육안 기반 부식 평가는 관찰자의 주관 및 조명 등 환경적 요인에 의해 객관성이 저하되는 한계가 있다. 이에 본 연구는 온실의 핵심 연결 부품인 새들 클램프(Saddle clamp)를 대상으로 적청(Red Rust)의 등급을 정량적으로 평가하기 위한 영상 처리 기반 자동화 알고리즘을 제안하였다. 가속 부식 거동 분석을 위해 중성 염수분무시험(NSS test) 24시간에서 120시간까지 수행하여 시편을 획득하였으며, 일정한 조명 및 촬영 조건 하에서 디지털 영상을 수집하였다. 배경 노이즈 및 초기 백청(White Rust) 오탐지를 배제하고자 전처리와 HSV-RGB 복합 필터링을 적용하여 실제 적청 영역만을 추출하였다. 제안된 알고리즘은 KS D 9502 RN 표준도와의 검증에서 높은 수치적 일치도를 보여 정량적 평가 도구로서의 활용 잠재력을 확인하였다. 본 연구 결과는 향후 온실 구조물의 자동화된 부식 모니터링 및 체계적인 유지관리 시스템 구축을 위한 기초 자료로 활용될 수 있을 것으로 기대된다.

키워드

HSV-RGB 이미지 분석

레이팅 넘버

부식 면적 비율

새들 클램프

염수분무시험

References

Ali AAIM, Jamaludin S, Imran MMH, Ayob AFM, Ahmad SZAS, Akhbar MFA, Suhrab MIR, Ramli MR (2023) Computer vision and image processing approaches for corrosion detection. J Mar Sci Eng 11:1954. https://doi.org/10.3390/jmse11101954

10.3390/jmse11101954

Apostolopoulos A, Matikas T (2018) The impact of corrosion and inelastic buckling on low cycle fatigue life of steel bars. Procedia Struct Integr 10:49-58. https://doi.org/10.1016/j.prostr.2018.09.008

10.1016/j.prostr.2018.09.008

ASTM (2003) ASTM G1 standard practice for preparing, cleaning, and evaluation corrosion test specimens.

Autengruber R, Luckeneder G, Hassel AW (2012) Corrosion of press-hardened galvanized steel. Corros Sci 63:12-19. https://doi.org/10.1016/j.corsci.2012.04.048

10.1016/j.corsci.2012.04.048

Beaulieu LV, Legeron F, Langlois S (2010) Compression strength of corroded steel angle members. J Constr Steel Res 66:1366-1373. https://doi.org/10.1016/j.jcsr.2010.05.006

10.1016/j.jcsr.2010.05.006

Choi MK, Ryu HR, Cho MW, Yu IH (2018) Evaluation of steel-pipe connections in plastic greenhouse using bending test. Prot Hortic Plant Fact 27:391-399. https://doi.org/10.12791/KSBEC.2018.27.4.391

10.12791/KSBEC.2018.27.4.391

Das A, Dorafshan S, Kaabouch N (2024) Autonomous image-based corrosion detection in steel structures using deep learning. Sensors 24:3630. https://doi.org/10.3390/s24113630

10.3390/s2411363038894421PMC11175235

Dhonde K, Mirhassani M, Tam E, Sawyer-Beaulieu S (2022) Design of a real-time corrosion detection and quantification protocol for automobiles. Materials 15:3211. https://doi.org/10.3390/ma15093211

10.3390/ma1509321135591545PMC9104952

El-Mahdy GA, Nishikata A, Tsuru T (2000) Electrochemical corrosion monitoring of galvanized steel under cyclic wet-dry conditions. Corros Sci 42:183-194. https://doi.org/10.1016/S0010-938X(99)00057-8

10.1016/S0010-938X(99)00057-8

Finšgar M, Jackson J (2014) Application of corrosion inhibitors for steels in acidic media for the oil and gas industry: a review. Corros Sci 86:17-41. https://doi.org/10.1016/j.corsci.2014.04.044

10.1016/j.corsci.2014.04.044

Hosking NC, Ström MA, Shipway PH, Rudd CD (2007) Corrosion resistance of zinc-magnesium coated steel. Corros Sci 49:3669-3695. https://doi.org/10.1016/j.corsci.2007.03.032

10.1016/j.corsci.2007.03.032

Khayatazad M, De Pue L, De Waele W (2020) Detection of corrosion on steel structures using automated image processing. Dev Built Environ 3:100022. https://doi.org/10.1016/j.dibe.2020.100022

10.1016/j.dibe.2020.100022

Kim B, Kim Y, Yang J (2021) Detection of corrosion on steel plate by using image segmentation method. J Korean Inst Surf Eng 54:84-89.

10.5695/JKISE.2021.54.2.84

Koo HJ, Choi KS (2016) A study on tensile test of crossing pipe clip for boundary conditions analysis of greenhouse connections. Proc Archit Inst Korea 36:522-523.

Korean Agency for Technology and Standards (2022) Methods of salt spray testing (KS D 9502). Korean Standards Association, Seoul, Korea.

Lazanas AC, Prodromidis MI (2023) Electrochemical impedance spectroscopy—A tutorial. ACS Meas Sci Au 3:162-193. https://doi.org/10.1021/acsmeasuresciau.2c00070

10.1021/acsmeasuresciau.2c0007037360038PMC10288619

Maraveas C (2020) Durability issues and corrosion of structural materials and systems in farm environment. Appl Sci 10:990. https://doi.org/10.3390/app10030990

10.3390/app10030990

Naik DL, Sajid HU, Kiran R, Chen G (2020) Detection of corrosion-indicating oxidation product colors in steel bridges under varying illuminations, shadows, and wetting conditions. Metals 10:1439. https://doi.org/10.3390/met10111439

10.3390/met10111439

Paterlini L, Brenna A, Ceriani F, Gamba M, Ormellese M, Bolzoni F (2024) Atmospheric corrosion of different steel types in urban and marine exposure. Materials 17:6211. https://doi.org/10.3390/ma17246211

10.3390/ma1724621139769812PMC11679332

Popova K, Prošek T (2022) Corrosion monitoring in atmospheric conditions: a review. Metals 12:171. https://doi.org/10.3390/met12020171

10.3390/met12020171

Rahgozar R, Sharifi Y (2011) Remaining fatigue life of corroded steel structural members. Adv Struct Eng 14:881-890. https://doi.org/10.1260/1369-4332.14.5.881

10.1260/1369-4332.14.5.881

Xia R, Jia C, Liu C, Liu P, Zhang S (2023) Non-uniform corrosion characteristics of the steel pipe pile exposed to marine environments. Ocean Eng 272:113873. https://doi.org/10.1016/j.oceaneng.2023.113873

10.1016/j.oceaneng.2023.113873

Information

Publisher :The Korean Society for Bio-Environment Control
Publisher(Ko) :(사)한국생물환경조절학회
Journal Title :Journal of Bio-Environment Control
Journal Title(Ko) :생물환경조절학회지
Volume : 35
No :3
Pages :241-255
Received Date : 2026-05-04
Revised Date : 2026-07-16
Accepted Date : 2026-07-28
DOI :https://doi.org/10.12791/KSBEC.2026.35.3.241

Journal of Bio-Environment Control ISSN:1229-4675(Print) 2765-3641(Online) 생물환경조절학회지

All Issue