• Journal of Korean Tunnelling and Underground Space Association
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• v.22 no.5
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• pp.515-528
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• 2020

Underground concrete structures are usually designed to be used for decades, but in recent years, many of them are nearing their original life expectancy. As a result, it is necessary to promptly inspect and repair the structure, since it can cause lost of fundamental functions and bring unexpected problems. Therefore, personnel-based inspections and repairs have been underway for maintenance of underground structures, but nowadays, objective inspection technologies have been actively developed through the fusion of deep learning and image process. In particular, various researches have been conducted on developing a concrete crack detection algorithm based on supervised learning. Most of these studies requires a large amount of image data, especially, label images. In order to secure those images, it takes a lot of time and labor in reality. To resolve this problem, we introduce a method to increase the accuracy of crack area detection, improved by 0.25% on average by applying adversarial learning in this paper. The adversarial learning consists of a segmentation neural network and a discriminator neural network, and it is an algorithm that improves recognition performance by generating a virtual label image in a competitive structure. In this study, an efficient deep neural network learning method was proposed using this method, and it is expected to be used for accurate crack detection in the future.

• Journal of the Society of Disaster Information
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• v.14 no.4
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• pp.458-465
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• 2018

Purpose: Concrete track that affects on railway safety can detect cracks using image processing technique. However, since a condition of concrete track and surface noisy are obstructed to detect cracks, there is a need for a way to remove them effectively. Method: In this study, we proposed an image processing to detect cracks effectively for Korean railway and verified its performance through experiment. We developed image acquisition system for capture a railway concrete track and acquired railway concrete track images, randomly selected 2000 images and detected cracks in the image process using proposed Gabor Filter Bank methods. Results: As a result, 94% of detection rate are matched to the actual cracks in same quality and format railway concrete track image. Conclution: The crack detection method using Garbor Filter Bank was confirmed to be effective for crack image including noise in the Korean railway concrete track. This system is expected to become an automated maintenance system in the existing human-centered railway industry.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.155-163
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• 2019

This paper proposes a new methodology to recognize cracks on asphalt road surfaces using the image data obtained with drones. The target section was Yuseong-daero, the main highway of Daejeon. Furthermore, two object detection algorithms, such as Tiny-YOLO-V2 and Faster-RCNN, were used to recognize cracks on road surfaces, classify the crack types, and compare the experimental results. As a result, mean average precision of Faster-RCNN and Tiny-YOLO-V2 was 71% and 33%, respectively. The Faster-RCNN algorithm, 2Stage Detection, showed better performance in identifying and separating road surface cracks than the Yolo algorithm, 1Stage Detection. In the future, it will be possible to prepare a plan for building an infrastructure asset-management system using drones and AI crack detection systems. An efficient and economical road-maintenance decision-support system will be established and an operating environment will be produced.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.7
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• pp.9-15
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• 2019

The purpose of present study is to assess the maximum width of the surface cracks using the histogram analysis of image processing techniques in concrete structures. For this purpose, the concrete crack image is acquired by the camera. The image is Grayscale coded and Binary coded. After Binary coded image is Dilate and Erode coded, the image is then recognized as separated objects by applying Labeling techniques. Over time, dust and stains may occur naturally on the surface of concrete. The crack image of concrete may include shadows and reflections by lighting depending on a surrounding conditions. In general, concrete cracks occur in a continuous pattern and noise of image appears in the form of shot noises. Bilateral Blurring and Adaptive Threshold apply to the Grayscale image to eliminate these effects. The remaining noises are removed by the object area ratio to the Labeled area. The maximum numbers of pixels and its positions in the crack objects without noises are calculated in x-direction and y-direction by Histogram analysis. The widths of the crack are estimated by trigonometric ratio at the positions of the pixels maximum numbers for the Labeled objects. Finally, the maximum crack width estimated by the proposed method is compared to the crack width measured with the crack gauge. The proposed method by the present study may increase the reliability for the estimation of maximum crack width using image processing techniques in concrete surface images.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.801-802
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• 2009

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.4
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• pp.398-405
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• 2001

The rivet joint has typical structural feature that can be initiation site for the fatigue crack due to the combination of local stress concentration around rivet hole and the moisture trapping. From a viewpoint of structural assurance, it is crucial to evaluate the size of crack around the rivet holes by appropriate nondestructive evaluation techniques. Lamb wave that is one of guided waves, offers a more efficient tool for nondestructive inspection of plates. The neural network that is considered to be the most suitable for pattern recognition has been used by researchers in NDE field to classify different types of flaws and flaw sizes. In this study, clack size evaluation around the rivet hole using the neural network based on the back-propagation algorithm has been tarried out by extracting some features from the ultrasonic Lamb wave for A12024-T3 skin panel of aircraft. Special attention was paid to reduce the coupling effect between the transducer and the specimen by extracting some features related to time md frequency component data in ultrasonic waveform. It was demonstrated clearly that features extracted from the time and frequency domain data of Lamb wave signal were very useful to determine crack size initiated from rivet hole through neural network.

• Journal of the Korean Society for Nondestructive Testing
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• v.15 no.2
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• pp.395-406
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• 1995

Classification of flaws in weldments from their ultrasonic scattering signals is very important in quantitative nondestructive evaluation. This problem is ideally suited to a modern ultrasonic pattern recognition technique. Here brief discussion on systematic approach to this methodology is presented including ultrasonic feature extraction, feature selection and classification. A stronger emphasis is placed on probabilistic neural networks as efficient classifiers for many practical classification problems. In an example probabilistic neural networks are applied to classify flaws in weldments into 3 classes such as cracks, porosity and slag inclusions. Probabilistic nets are shown to be able to exhibit high performance of other classifiers without any training time overhead. In addition, forward selection scheme for sensitive features is addressed to enhance network performance.

• Proceedings of the KSEG Conference
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• 2004.03a
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• pp.16001-16125
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• 2004

물리검층은 주로 석유탐사 분야에서 개발·이용되어왔으나 소구경 시추공을 이용하는 지반조사. 자원평가, 지하수조사 또는 환경오염조사 등에서 물리검층의 수요가 증가하고 있다. 국내에서 수행된 물리검층은 우라늄 탐사 목적으로 1980년 전후로 많이 수행되었으며 1990년 중반에는 지하수 조사에 일부 활용되어 왔다. 현재 물리검층의 활용도가 증가하고 있는 분야는 지반조사 분야로 원위치 물성측정, 시추공과 교차하는 암반의 균열파악 등 터널, 교량과 같은 각종 토목분야의 지반조사에 많이 활용되고 있다. 이외에도 지하수 유동특성 규명이나 환경오염조사, 자원평가(지열, 온천, 석·골재, 금속광상 등)에도 활용되고 있다. (중략)

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 2002.05a
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• pp.491-496
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• 2002

중력식 댐과 같은 대형구조체의 콘크리트에만 적용되는 것으로 인식되어 왔던 매스콘크리트의 개념적용은 최근 콘크리트 구조물이 대형화, 특수화되어 유동성이 충분히 확보된 콘크리트의 대량 급속 시공이 증가하고 있어 그 특별한 범주를 규정하기 힘든 실정이다. 이러한 현실 뿐만아니라 과거보다는 상대적으로 고강도화된 콘크리트가 요구되고 있기 때문에 빈번히 관찰되고 있는 매스콘크리트 구조물의 균열발생원인 및 발생시기의 예측이 어려워지고 있다.(중략)

• Proceedings of the Korean Society for Rock Mechanics Conference
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• 1995.03a
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• pp.92-100
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• 1995

최근 지하 공간의 활용이 증대되는 추세에 이와 관련되어 지하 구조물의 시공전 또는 시공 중에 있어 시공 전략 수립, 장비 결정, 안정성 및 경제성 등을 위해 암반 내의 파쇄대나 균열대의 발달 및 분포에 대한 탐사는 필수적으로 선행되어야 한다. 이러한 탐사에는 분해능이 매우 높은 탐사 기술이 요구되며, 일반적으로 시추공이나 지하 갱도를 이용한 탄성파 탐사나 지오레이다(georadar) 탐사를 많이 사용하고, 자료해석을 위한 역산 기법으로는 지오토모그래피(geotomography)가 필수적인 기법으로 인식되고 있다. (중략)