• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.93-106
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• 2021

Many recent studies on deep learning-based road crack detection have shown significantly more improved performances than previous works using algorithm-based conventional approaches. However, many deep learning-based studies are still focused on classifying the types of cracks. The classification of crack types is highly anticipated in that it can improve the crack detection process, which is currently relying on manual intervention. However, it is essential to calculate the severity of the cracks as well as identifying the type of cracks in actual pavement maintenance planning, but studies related to road crack detection have not progressed enough to automated calculation of the severity of cracks. In order to calculate the severity of the crack, the type of crack and the area of the crack in the image must be identified together. This study deals with a method of using Mobilenet-SSD that is deep learning-based object detection techniques to effectively automate the simultaneous detection of crack types and crack areas. To improve the accuracy of object-detection for road cracks, several experiments were conducted to combine the U-Net for automatic segmentation of input image and object-detection model, and the results were summarized. As a result, image masking with U-Net is able to maximize object-detection performance with 0.9315 mAP value. While referring the results of this study, it is expected that the automation of the crack detection functionality on pave management system can be further enhanced.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.649-652
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• 2021

본 논문에서는 노후화된 목조·석조 건축물의 균열을 탐지하는 기법을 소개한다. 본 기법의 목적은 석조·목조 문화재의 시간의 흐름에 따른 관리 소홀, 균열(벌레, 날씨, 기온 등), 배부름 현상에 의한 문화재의 손상을 사전에 방지하기 위함이다. 기존에 존재하는 목조·석조 건축물의 균열, 노후, 배부름 등 다양한 결함과 변형의 탐지 방법은 접촉식 센서를 이용하여 탐지를 해왔지만, 문화재 자체의 미관을 해칠 뿐 아니라 문화재를 추가로 훼손할 가능성이 있다는 문제점이 제시되었다. 이 문제를 해결하기 위해 문화재 비 접촉형 탐지 기법을 사용한다. CCTV 및 DSLR과 같은 관측장비로 촬영한 영상정보를 기반으로 문화재의 결함과 변형을 AI 영상분석 기반 방법으로 판단하는 문제를 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.996-999
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• 2020

노후 선박의 증가로 선체 검사의 필요성이 높아지고 있다. 하지만 선체 벽면의 균열을 찾고 보수하는 작업은 위험성이 높고 효율성이 낮다. 이에 본 논문에서는 선체의 벽면에 진공 흡착하여 장애물에 부딪히지 않고 선체 벽면을 이동하면서 균열을 탐지하는 로봇을 개발하였다. 선체 균열탐지 로봇은 선체뿐만 아니라 사람이 직접 균열을 찾기 힘들거나 위험한 곳에 유용할 것이며 균열로 인한 선박 사고 발생을 줄여줄 것으로 기대된다.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.05b
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• pp.104-109
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• 1997

인코넬600 합금을 열처리상태 및 변형속도등이 서로 다른 응력부식균열(SCC) 발생 조건하에서 정변형속도 시험법으로 인장시켜 그때 발생되는 AE신호와 부식전류를 측정하여 균열거동과 비교하므로서 SCC 발생 및 진전을 AE로서 적절히 탐지할 수 있는가를 연구하였다. 그 결과 SCC. 연성파괴 및 기계적인 변형에서 발생되는 AE는 amplitude 준위에 의해 식별가능하며, 이것은 AE amplitude 준위가 AE발생원을 식별할 수 있는 중요한 변수가 될 수 있음을 의미한다. 또한 AE 발생시점과 전기 화학적 전류변동이 들 일치하는 것으로 나타나 입계응력부식 균열 진전이 AE에 의해 적절히 탐지될 수 있음을 알 수 있다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.35-42
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• 2020

In this study, we propose a crack detection method using limited data with a U-Net based image inpainting technique that is a modified unsupervised anomaly detection method. Concrete cracking occurs due to a variety of causes and is a factor that can cause serious damage to the structure in the long term. In general, crack investigation uses an inspector's visual inspection on the concrete surfaces, which is less objective in judgment and has a high possibility of human error. Therefore, a method with objective and accurate image analysis processing is required. In recent years, the methods using deep learning have been studied to detect cracks quickly and accurately. However, when the amount of crack data on the building or infrastructure to be inspected is small, existing crack detection models using it often show a limited performance. Therefore, in this study, an unsupervised anomaly detection method was used to augment the data on the object to be inspected, and as a result of learning using the data, we confirmed the performance of 98.78% of accuracy and 82.67% of harmonic average (F1_Score).

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1994.10a
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• pp.187-191
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• 1994

기계구조물내의 균열은 고하중상태에서 갑작스러운 파괴의 주요 원인의 하나로서 이러한 균열의 조기탐지를 위해 기존의 비파괴검사 방법 이외에, 최근 진동측정 및 진동분석을 이용하는 방법이 경제성 및 그 효용성으로 인하여 깊게 연구되고 있다. 이러한 진동특성의 변화를 이용하여 균열을 탐지하는 방법이 많은 학자들에 의해 연구되어졌으며, 현재까지의 연구결과중 균열의 크기 및 위치를 동시에 탐지할 수 있는 방법중에서 비교적 단순, 정확하다고 판단되는 방법으로는 임의의 두 지점에서의 진폭측정을 이용한 Rizos(1)의 방법과 고유진동수 및 모우드형 측정을 이용한 Kam & Lee(2)의 방법이 있으나 이들 방법은 두가지 이상의 진동특성치를 요구하고 있다. 본 연구의 목적은 진동특성치중 고유진동수만을 이용하여 단순부재에서 균열의 크기 및 위치를 수치적으로 예측할 수 있는 새로운 해석기법을 제시하고, 기존 방법 사용시의 결과와 비교 검토하여 그 유용성을 판단하는데 있다.

• Journal of the Korean GEO-environmental Society
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• v.22 no.10
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• pp.13-19
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• 2021

Cracks in asphalt pavement occur due to changes in weather or impact from vehicles, and if cracks are left unattended, the life of the pavement may be shortened, and various accidents may occur. Therefore, studies have been conducted to detect cracks through images in order to quickly detect cracks in the asphalt pavement automatically and perform maintenance activity. Recent studies adopt machine-learning models for detecting cracks in asphalt road pavement using a Convolutional Neural Network. However, their practical use is limited because they require high-performance computing power. Therefore, this paper proposes a framework for detecting cracks in asphalt road pavement by applying a small deep learning model applicable to mobile devices. The small deep learning model proposed through the case study was compared with general deep learning models, and although it was a model with relatively few parameters, it showed similar performance to general deep learning models. The developed model is expected to be embedded and used in mobile devices or IoT for crack detection in asphalt pavement.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.1
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• pp.54-61
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• 2000

In order to determine the mode I stress intensity factor ($K_I$) by means of the alternating current potential drop(ACPD) technique, the change in potential drop due to load for a paramagnetic material containing a two-dimensional surface crack was examined. The cause of the change in potential drop and the effects of the magnetic flux and the demagnetization on the change in potential drop were clarified by using the measuring systems with and without removing the magnetic flux from the circumference of the specimen. The change in potential drop was linearly decreased with increasing the tensile load and was caused by the change in conductivity near the crack tip. The reason of decreasing the change in potential drop with increasing the tensile load was that the increase of the conductivity near the crack tip due to the tensile load caused the decreases of the resistance and internal inductance of the specimen. The relationship between the change in potential drop and the change in $K_I$ was not affected by demagnetization and was independent of the crack length.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.4
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• pp.65-74
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• 2021

In order to efficiently inspect rapidly increasing old tunnels in many well-developed countries, many inspection methodologies have been proposed using imaging equipment and image processing. However, most of the existing methodologies evaluated their performance on a clean concrete surface with a limited area where other objects do not exist. Therefore, this paper proposes a 6-step framework for tunnel crack detection deep learning model development. The proposed method is mainly based on negative sample (non-crack object) training and Cascade Mask R-CNN. The proposed framework consists of six steps: searching for cracks in images captured from real tunnels, labeling cracks in pixel level, training a deep learning model, collecting non-crack objects, retraining the deep learning model with the collected non-crack objects, and constructing final training dataset. To implement the proposed framework, Cascade Mask R-CNN, an instance segmentation model, was trained with 1561 general crack images and 206 non-crack images. In order to examine the applicability of the trained model to the real-world tunnel crack detection, field testing is conducted on tunnel spans with a length of about 200m where electric wires and lights are prevalent. In the experimental result, the trained model showed 99% precision and 92% recall, which shows the excellent field applicability of the proposed framework.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.15-22
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• 2020

In this study, we propose an improvement method that can create U-Net model which detect fine concrete cracks by applying a weighted loss function. Because cracks in concrete are a factor that threatens safety, it is important to periodically check the condition and take prompt initial measures. However, currently, the visual inspection is mainly used in which the inspector directly inspects and evaluates with naked eyes. This has limitations not only in terms of accuracy, but also in terms of cost, time and safety. Accordingly, technologies using deep learning is being researched so that minute cracks generated in concrete structures can be detected quickly and accurately. As a result of attempting crack detection using U-Net in this study, it was confirmed that it could not detect minute cracks. Accordingly, as a result of verifying the performance of the model trained by applying the suggested weighted loss function, a highly reliable value (Accuracy) of 99% or higher and a harmonic average (F1_Score) of 89% to 92% was derived. The performance of the learning improvement plan was verified through the results of accurately and clearly detecting cracks.