• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.53-56
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• 2021

국내 산업화가 들어선 후 산업화 당시 지었던 낙후된 건물의 증가에 따라 구조물의 손상 조사 및 검사 방법의 수요가 늘어나고 있다. 일반적으로 구조물의 손상은 전문 검사원이 현장에서 직접 측량도구와 시각적인 방식으로 검사한다. 그러나 전문 검사원들이 직접 조사하는 수고에 비해 균열을 검사하는 방식 자체가 단순하고, 일반 사람이 검사하기에는 객관성이 떨어지는 한계가 있어 균열을 자동적으로 검출함으로써 객관성과 편의성을 보장할 기술이 필요하다. 본 연구에서는 이미지 기반으로 다양한 환경에서의 외벽 균열을 검출할 수 있는 딥러닝 모델 개발을 소개한다. 균열 검출을 위해 다양한 외벽 균열 관련 데이터셋을 확보 및 구축하고 각 데이터셋의 검출 정보를 보완할 반자동(semi-auto) 라벨링 작업을 수행하였다. 두 번째로 기존 높은 검출 성능을 보였던 모델들을 선정 및 비교하여 YOLO v5 모델을 최종적으로 선정하였고, 도메인이 각각 다른 데이터셋에 대한 교차 학습을 통해 각 데이터셋의 mAP의 편차가 31%에서 11%로 좁히는 작업을 수행하였다. 이를 통해 실제 상황에서의 균열 영상에서 균열을 검출할 수 있는 측량 시스템을 개발함으로써 실질적인 검사의 도구로 활용될 수 있길 기대한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.576-579
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• 2004

Eventhough the productivity has been improved remarkably by introducing automatic facilities, the 100％ inspection is necessary because the possibility to produce large amount of defective goods is also increased. Since it is extremely unreasonable that workers inspect very large amount of products as 100％ inspection, there has been many researches for the automatic inspection system. In this thesis, we develop an automatic detection system of suspension insulator's minutes cracks System The automatic detection system of suspension insulator's minute cracks： To detect the minute cracks of suspension insulators, images of the insulator are acquired with a progressive scan camera, rotating a suspension insulator on a turning table. And after the shadow and noises are eliminated by preprocessing techniques, we detect minute cracks using the features of them.

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.5
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• pp.356-361
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• 2014

The eddy current testing performance directly affects the results of a steam generator tube integrity assessment because the integrity assessment of defected tubes is conducted based on eddy current testing results. This means that it may not be possible to accurately discriminate between adjacent flaws. This paper presents an investigation on the resolution of rotating pancake coils with multiple cracks and the effects on the structural integrity assessment of steam generator tubes.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.2
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• pp.98-103
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• 2009

The nuclear power plants have been safely operated by plugging the steam generator tubes which have the crack indications. Tube rupture events can occur if analysts fail to detect crack indications during in-service inspection. There are various types of crack indication in steam generator tubes and they have been detected by the eddy current test. The integrity assessment should be performed using the crack-sizing results from eddy current data when the crack indication is detected. However, it is not easy to evaluate the crack-depth precisely and consistently due to the complexity of the methods. The current crack-sizing methods were reviewed in this paper and the suitable ones were selected through the laboratory tests. The retired steam generators of Kori Unit 1 were used for this study. The round robin tests by the domestic qualified analysts were carried out and the statistical models were introduced to establish the appropriate depth-sizing techniques. It is expected that the proposed techniques in this study can be utilized in the Steam Generator Management Program.

• Journal of the Korean Geotechnical Society
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• v.34 no.12
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• pp.145-154
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• 2018

The damage investigation and inspection methods performed in concrete facilities such as bridges, tunnels, retaining walls and so on, are usually visually examined by the inspector using the surveying tool in the field. These methods highly depend on the subjectivity of the inspector, which may reduce the objectivity and reliability of the record. Therefore, the new image processing techniques are necessary in order to automatically detect the cracks and objectively analyze the characteristics of cracks. In this study, deep learning and image processing technique were developed to detect cracks and analyze characteristics in images for concrete facilities. Two-stage image processing pipeline was proposed to obtain crack segmentation and its characteristics. The performance of the method was tested using various crack images with a label and the results showed over 90% of accuracy on crack classification and segmentation. Finally, the crack characteristics (length and thickness) of the crack image pictured from the field were analyzed, and the performance of the developed technique was verified by comparing the actual measured values and errors.

• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.36-44
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• 2024

This paper presents the application of Convolutional Neural Networks (CNNs) and Region of Interest (ROI) techniques for concrete crack analysis. Surfaces of concrete structures, such as beams, etc., are exposed to fatigue stress and cyclic loads, typically resulting in the initiation of cracks at a microscopic level on the structure's surface. Early detection enables preventative measures to mitigate potential damage and failures. Conventional manual inspections often yield subpar results, especially for large-scale infrastructure where access is challenging and detecting cracks can be difficult. This paper presents data collection, edge segmentation and ROI techniques application, and analysis of concrete cracks using Convolutional Neural Networks. This paper aims to achieve the following objectives: Firstly, achieving improved accuracy in crack detection using image-based technology compared to traditional manual inspection methods. Secondly, developing an algorithm that utilizes enhanced Sobel edge segmentation and ROI techniques. The algorithm provides automated crack detection capabilities for non-destructive testing.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.65-68
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• 2021

본 연구에서는 Dilated U-Net 기반의 이미지 복원기법을 통해 콘크리트 균열 추출 성능 개선 방안을 제안한다. 콘크리트 균열은 구조물의 미관상의 문제뿐 아니라 추후 큰 안전사고의 원인이 될 수 있어 초기대응이 중요하다. 현재는 점검자가 직접 육안으로 검사하는 외관 검사법이 주로 사용되고 있지만, 이는 정확성 및 비용, 시간, 그리고 안전성 면에서 한계를 갖고 있다. 이에 콘크리트 구조물 표면에 대해 획득한 영상 처리 기법을 사용한 검사 방식 도입의 관심이 늘어나고 있다. 또한, 딥러닝 기술의 발달로 딥러닝을 적용한 영상처리의 연구 역시 활발하게 진행되고 있다. 본 연구는 콘크리트 균열 추개선출 성능 개선을 위해 Dilated U-Net 기반의 이미지 복원기법을 적용하는 방안을 제안하였고 성능 검증 결과, 기존 U-Net 기반의 정확도가 98.78%, 조화평균 82.67%였던 것에 비해 정확도 99.199%, 조화평균 88.722%로 성능이 되었음을 확인하였다.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.5
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• pp.493-500
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• 2009

An ultrasonic signal processing algorithm was developed for extracting the information of cracks generated around the keyway of a turbine rotor disk. B-scan images were obtained by using keyway specimens and an ultrasonic scan system with x-y position controller. The B-scan images were used as input images for 2-Dimensional signal processing, and the algorithm was constructed with four processing stages of pre-processing, crack candidate region detection, crack region classification and crack information extraction. It is confirmed by experiments that the developed algorithm is effective for the quantitative evaluation of cracks generated around the keyway of turbine rotor disk.

• Journal of the Korean Society for Nondestructive Testing
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• v.33 no.2
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• pp.187-192
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• 2013

Fatigue crack was detected from a temperature change around surface crack using the thermographic technique. Thermal gradient across the crack decreased very much due to thermal resistance of contact surface in the crack. Heat diffusion flow passing through the discontinuity was visualized in temperature by infrared camera to find and locate the crack. A fatigue crack specimen(SM-45C), which was prepared according to KS specification and notched in its center to initiate fatigue crack from the notch tip, was heated by halogen lamp at the end of one side to generate a heat diffusion flow in lateral direction. A abrupt jump in temperature across the fatigue crack was observed in thermographic image, by which the crack could be located and sized from temperature distribution.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.2
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• pp.180-185
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• 2004

Conventional ultrasonic examination to detect micro and small surface cracks is based on the pulse-echo technique using a normal immersion focused transducer with high frequency, or an angle-beam transducer generating surface waves. It is difficult to make an automatic ultrasonic system that can detect micro and small surface cracks and position in a large structure like steel and ceramic rolls, because of the huge data of inspection and the ambiguous position data of the transducer. In this study, a high-precision scanning acoustic microscope with a 10MHz large-aperture transducer has been used to assess the existence, position and depth of a surface crack from the real-time A, B, C scans obtained by exploiting the ultrasonic diffraction. The ultrasonic method with large aperture transducer has improved the accuracy of the crack depth assessment and also the scanning speed by ten times, compared with the conventional ultrasonic methods.