• Journal of the Korea Institute of Building Construction
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• v.23 no.2
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• pp.197-207
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• 2023

Concrete is a widely used material due to its excellent compressive strength and durability. However, depending on the surrounding environment and the characteristics of the materials used in the construction, various defects may occur, such as cracks on the surface and subsidence of the structure. The detects on the surface of the concrete structure occur after completion or over time. Neglecting these cracks may lead to severe structural damage, necessitating regular safety inspections. Traditional visual inspections of concrete walls are labor-intensive and expensive. This research presents a deep learning-based semantic segmentation model designed to detect cracks in concrete walls. The model addresses surface defects that arise from aging, and an image augmentation technique is employed to enhance feature extraction and generalization performance. A dataset for semantic segmentation was created by combining publicly available and self-generated datasets, and notable semantic segmentation models were evaluated and tested. The model, specifically trained for concrete wall fracture detection, achieved an extraction performance of 81.4%. Moreover, a 3% performance improvement was observed when applying the developed augmentation technique.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.65-73
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• 2023

In this paper, we propose a data augmentation method based on CNN(Convolutional Neural Network) learning for efficiently obtaining concrete crack image datasets. Real concrete crack images are not only difficult to obtain due to their unstructured shape and complex patterns, but also may be exposed to dangerous situations when acquiring data. In this paper, we solve the problem of collecting datasets exposed to such situations efficiently in terms of cost and time by using vector and thickness-based data augmentation techniques. To demonstrate the effectiveness of the proposed method, experiments were conducted in various scenes using U-Net-based crack detection, and the performance was improved in all scenes when measured by IoU accuracy. When the concrete crack data was not augmented, the percentage of incorrect predictions was about 25%, but when the data was augmented by our method, the percentage of incorrect predictions was reduced to 3%.

• 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.

• 한국도로학회:학술대회논문집
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• 2002.10a
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• pp.195-198
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• 2002

• 한국지형공간정보학회:학술대회논문집
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• 2002.11a
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• pp.93-99
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• 2002

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.1
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• pp.31-35
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• 2010

This paper presents the development of automatic defect detection program using 3D computed tomography image of small solid rocker motor. We applied the neighbor pixel comparison algorithm with beam hardening correction for the recognition of defect. We made the artificial defect specimen in order to decide a standard CT value of defect. The program was tested with 150 small solid rocket motors and it could detect the disbond, crack, foreign material and void. The program showed more reliable and faster results than human inspector's interpretation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.7
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• pp.1575-1581
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• 2005

Recently, further study is being done on the affect of crack on concrete structure and many people have made every endeavor not to leave it unsettled but to minimize it by repair works. In this paper we propose the image processing method that do not remain manual but automatically process the length, the direction and e width of cracks on concrete surface. First, we calibrate light's affect from image by using closing operation, one of morphology methods that can extract the feature of oracle and we extract the edge of crack image by sobel mask. After it, crack image is binarized by iteration binarization. And we extract the edge of cracks using noise elimination method that use an average of adjacent pixels by 3${\times}$3 mask and Glassfire Labeling algorithm. on, in this paper we propose an image processing method which can automatically measure the length, the direction and the width of cracks using the extracted edges of cracks. The results of experiment showed that the proposed method works better on the extraction of concrete cracks. Also our method showed the possibility that inspector's decision is unnecessary.

• Journal of Korean Society of Steel Construction
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• v.9 no.3 s.32
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• pp.287-300
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• 1997

In this study, an effective application method of AE technique for the detection of fatigue crack in multi-girder steel bridges has been proposed. The applicability has been examined through the laboratory works with bridge model. The proposed analytical method which evaluates the remaining fatigue lives of structural members may improve the rational determination of the priority of inspection for structural members assuming to have fatigue cracks. Laboratory tests for the application of AE technique to steel girder bridges show that the frequency bands of traffic noise are in the range between 10 show that the frequency bands of traffic noise are in the range between 100~200 kHz and the AE signal raised from fatigue cracks is concentrated around 400~500 kHz. Therefore. R30 sensor is proved to be the most suitable for the detection of cracks in steel girder bridges. A linear proportionality between the crack propagation and the frequency of AE signals has been obtained. In addition, an economic and effective source location method for steel girder bridges was studied through experiments.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.1
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• pp.45-52
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• 2003

A number of stress corrosion cracks in turbine rotor disk keyway in power plants have been found and the necessity has been raised to detect and evaluate the cracks prior to the catastrophic failure of turbine disk. By ultrasonic RF signal analysis and using a neural network based on bark-propagation algorithm, we tried to evaluate the location, size and orientation of cracks around keyway. Because RF signals received from each reflector have a number of peaks, they were processed to have a single peak for each reflector. Using the processed RF signals, scan data that contain the information on the position of transducer and the arrival time of reflected waves from each reflector were obtained. The time difference between each reflector and the position of transducer extracted from the scan data were then applied to the back-propagation neural network. As a result, the neural network was found useful to evaluate the location, size and orientation of cracks initiated from keyway.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.3
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• pp.147-153
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• 2018

Cracking in concrete structure must be examined according to appropriate methods, to ensure structural serviceability and to prevent structural deterioration, since cracks opened wide for a long time expedite corrosion of rebar. A site investigation is conducted in a regular basis to monitor structural deterioration by tracking growing cracks. However, the visual inspection are labor intensive. and judgment are subject. To overcome the limit of the on-site visual investigation image processing for identifying the cracks of concrete structures by analyzing 2D images has been developed. This study develops a unique 3D technique utilizing a line laser and its projection image onto concrete surfaces. Automated process of crack detection is developed by the algorithms of automatizing crack map generation and image data acquisition. Performance of the developed method is experimentally evaluated.