콘크리트 균열 깊이 추정에 유의미한 이미지 특성 변수에 관한 연구
A study on the meaningful characteristic variables of the image for estimating the depth of concrete cracks
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Since the depth of concrete cracks is important information for evaluating the safety of facilities, a technology capable of accurately and quickly measuring the depth of cracks is needed. However, in the facility maintenance practice, the crack depth is measured through non-destructive testing using ultrasonic equipment. This method has the disadvantage of poor efficiency. Such limitations of existing technologies can be solved through the development of image-based crack depth measurement technology. This is because if the crack depth measurement is based on an image, restrictions on the timing and location of the inspection can be resolved. In order for such image-based crack depth measurement technology to be developed, identification of image characteristic variables related to crack depth must precede. Therefore, the purpose of this study was to identify image characteristic variables meaningful to the crack depth. In order to achieve the purpose of the study, we collected 100 actual cases of cracking, measured the crack depth, and took an image. In addition, we created a dataset by extracting image characteristic variables such as hue, luminance, and brightness from the captured image through image processing technology. Using the thus-produced dataset, we conducted statistical analysis to identify significant image characteristic variables for crack depth estimation.
Abstract
Since the depth of concrete cracks is important information for evaluating the safety of facilities, a technology capable of accurately and quickly measuring the depth of cracks is needed. However, in the facility maintenance practice, the crack depth is measured through non-destructive testing using ultrasonic equipment. This method has the disadvantage of poor efficiency. Such limitations of existing technologies can be solved through the development of image-based crack depth measurement technology. This is because if the crack depth measurement is based on an image, restrictions on the timing and location of the inspection can be resolved. In order for such image-based crack depth measurement technology to be developed, identification of image characteristic variables related to crack depth must precede. Therefore, the purpose of this study was to identify image characteristic variables meaningful to the crack depth. In order to achieve the purpose of the study, we collected 100 actual cases of cracking, measured the crack depth, and took an image. In addition, we created a dataset by extracting image characteristic variables such as hue, luminance, and brightness from the captured image through image processing technology. Using the thus-produced dataset, we conducted statistical analysis to identify significant image characteristic variables for crack depth estimation.
참고문헌 (22)
- 국토교통부, "시설물의 안전 및 유지관리 실시 세부지침", ~ (2019)
- 김광백, "퍼지 기법을 이용한 콘크리트 표면의 균열 검출", 한국정보통신학회논문지, 14(6), pp.1353~1358 (2010), 한국정보통신학회
- 김아람, "딥러닝과 영상처리기법을 이용한 콘크리트 지반 구조물 균열 탐지", 한국지반공학회논문집, 34(12), pp.145~154 (2018), 한국지반공학회[DOI]
- 김윤경, "이미지 분석기법을 이용한 콘크리트 구조물의 결함 검출 시스템 개발", 한국정보기술학회논문지, 14(10), pp.163~168 (2016), 한국정보기술학회[DOI]
- 김정용, "이미지프로세싱기법을 이용한 균열인식 알고리즘 및 프로그램 개발", 대한토목학회 논문집D, 22(4-D), pp.639~647 (2002), 대한토목학회
- 김종우, "드론과 이미지 분석기법을 활용한 구조물 외관점검 기술 연구", 한국건축시공학회지, 17(6), pp.545~557 (2017), 한국건축시공학회[DOI]
- 변태보, "모폴로지 기반의 차영상 분석기법을 이용한 균열검출의 인식", 한국정보통신학회논문지, 10(1), pp.197~205 (2006), 한국정보통신학회
- 이방연, "개선된 이진화와 형상분석 기법을 응용한 콘크리트 표면 균열의 화상처리 알고리즘 개발", 콘크리트학회 논문집, 17(3), pp.361~368 (2005), 한국콘크리트학회
- 이재언, "개선된 영상처리기법을 이용한 콘크리트 표면 균열 추출 및 분석", 한국지능정보시스템학회학술대회논문집, pp.365~372 (2007)
- 이호범, "이미지 분석기법을 이용한 콘크리트 구조물의 균열 검출 시스템 개발", 한국구조물진단유지관리공학회 논문집, 16(1), pp.64~77 (2012), 한국구조물진단유지관리공학회
- 정서영, "딥러닝 및 영상처리 기술을 활용한 콘크리트 균열 검출 방법", 대한건축학회논문집 구조계, 35(11), pp.163~170 (2019), 대한건축학회
- 조수진, "딥러닝을 이용한 이미지기반의 콘크리트 균열 및 박락 탐지", 대한토목학회지, 66(8), pp.92~97 (2018)
- Allen Zhang, "Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network", Computer-Aided Civil Infrastructure Engineering, 32(10), pp.805~819 (2017)
- Young-Jin Cha, "Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks", Computer-Aided Civil and Infrastructure Engineering, 32(5), pp.361~378 (2017)
- Cao Vu Dung, "Autonomous concrete crack detection using deep fully convolutional neural network", Automation in Construction, 99, pp.52~58 (2019)
- 이동한, "Robust Concrete Crack Detection Using Deep Learning-Based Semantic Segmentation", International Journal of Aeronautical and Space Sciences, 20(1), pp.287~299 (2019), 한국항공우주학회[DOI]
- Gajanan K. Choudhary, "Crack detection in concrete surfaces using image processing, fuzzy logic, and neural networks", 2012 IEEE Fifth International Conference on Advanced Computational Intelligence(ICACI), pp.404~411 (2013)
- Liang-Chien Chen, "Mensuration of concrete cracks using digitized close-range photographs", Proc. 22nd Asian Conf. Remote Sens, pp.1248~1253 (2001)
- Kasthurirangan Gopalakrishnan, "Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection", Construction and Building Materials, 157, pp.322~330 (2017)
- Lei Zhang, "Road crack detection using deep convolutional neural network", 2016 IEEE International Conference on Image Processing(ICIP), pp.3708~3712 (2016)
- Wilson Ricardo Leal da Silva, "Concrete Cracks Detection Based on Deep Learning Image Classification", Proceedings of The Eighteenth International Conference of Experimental Mechanics, 2(8), pp.489~ (2018)
- Yusuke Fujita, "A Method for Crack Detection on a Concrete Structure", 18th International Conference on Pattern Recognition, 3, pp.901~904 (2006)