• Proceedings of the Korean Fiber Society Conference
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• 2003.04a
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• pp.315-316
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• 2003

차량의 통행이 빈번한 기존 아스팔트 도로는 연속적인 차량의 하중에 의한 스트레스로 하중을 받을 때마다 아스팔트도로 층의 강도와 안정성이 떨어지면서 균열이 발생하고 이 균열이 아스팔트 도로 상층부까지 전달되는 반사균열이 발생한고, 열팽창과 수축의 반복에 기인하는 상부의 아스팔트 층의 피로에 의한 균열이 발생한다. 따라서 아스팔트 도로의 반사균열을 억제하고, 아스팔트의 소성변형에 의한 rutting현상을 억제하여 아스팔트의 도로보수 주기를 연장하여 도로상에서의 잦은 보수에 의한 자동차의 정체현상을 줄이고 도로유지에 소요되는 비용을 절감하기 위해서 아스팔트 도로를 설치할 때 아스팔트를 보강시켜주는 geogrid의 사용이 보편화 되고 있다. (중략)

• 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 Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.173-175
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• 2010

본 논문에서는 콘크리트 표면에 발생한 균열의 미세한 부분까지 효과적으로 검출 할 수 있는 방법을 제안한다. 제안된 균열 검출 방법은 콘크리트 영상의 RGB값을 퍼지 기법에 적용하여 후보 균열 영역을 검출한다. 검출된 후보 균열 영역에서 밀도와 명암도를 이용하여 효과적으로 세부적인 잡음까지 제거한 후, 최종으로 균열 영역을 검출한다. 실제 콘크리트 표면의 균열 영상을 대상으로 실험한 결과, 제안된 방법이 기존의 방법보다 균열의 검출 성능이 개선되었음을 확인할 수 있었다.

• 한국도로학회지:도로
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• v.16 no.3
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• pp.54-63
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• 2014

최근 터널에서 균열, 누수 등과 같은 현상이 많이 발생하고 있어 터널의 안정성 및 균열보수, 미관, 고객의 공용 중 심리적 불안감을 조성한다는 것을 쉽게 찾을 수 있다. 고속국도 제30호선 상주~안동간은 16개소/12km의 터널이 설계되어 있어 터널의 비중이 크다. 그러므로 본 연구에서는 터널 라이닝 균열발생 원인분석 및 기존사례 조사를 통하여 균열발생 최소화 방안을 검토 및 터널 라이닝의 유해한 균열방지를 위하여 공사관계자(발주처, 감리사, 시공사, 하도급사 등) 의견수렴을 통한 여러 가지 대책공법을 제안하였으며 제안공법의 효율성을 판단하기 위해 고속국도 제30호선 상주~안동간 제 8공구의 단촌 2터널, 단촌 3터널에 시험시공을 실시하여 각 공법별 균열율 및 시공성, 경제성을 고려하여 결과를 분석하고 최적의 공법을 파악, 전파하는데 그 목적이 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 Korea Society of Industrial Information Systems
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• v.24 no.3
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• pp.23-29
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• 2019

Conventional crack detection methods have a problem of consuming a lot of labor, time and cost. To solve these problems, an automatic detection system is needed to detect cracks in images obtained by using vehicles or UAVs(unmanned aerial vehicles). In this paper, we have studied road crack detection with unmanned aerial photographs. Aerial images are generated through preprocessing and labeling to generate morphological information data sets of cracks. The generated data set was applied to the mask R-CNN model to obtain a new model in which various crack information was learned. Experimental results show that the cracks in the proposed aerial image were detected with an accuracy of 73.5% and some of them were predicted in a certain type of crack region.

• Journal of the Korean Geotechnical Society
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• v.17 no.4
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• pp.191-198
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• 2001

국내 도로포장의 주요 파손형태는 주변환경 및 반복 교통하중 조건에 의한 소성변형(rutting), 피로균열, 반사균열, 온도균열 등이 있는데, 포장이 설계수명에 도달하기 이전에 주로 발생하며 이로 인한 도로포장의 유지관리에 막대한 국가예산이 낭비되고 있는 실정이다. 본 연구에서는 토목섬유 아스팔트 포장 시스템을 체계적으로 정립하기 위해 휠트래킹 시험과 균열저항성 시험을 수행하여 토목섬유 아스팔트 포장의 소성변형 및 균열 저항성을 분석하였다. 이러한 실험결과를 통해 아스팔트 포장에서의 토목섬유 보강 효과가 평가되었다.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.6
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• pp.38-44
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• 2019

A Convolution Neural Network(CNN) model was utilized to detect surface cracks in asphalt concrete pavements. The CNN used for this study consists of five layers with 3×3 convolution filter and 2×2 pooling kernel. Pavement surface crack images collected by automated road surveying equipment was used for the training and testing of the CNN. The performance of the CNN was evaluated using the accuracy, precision, recall, missing rate, and over rate of the surface crack detection. The CNN trained with the largest amount of data shows more than 96.6% of the accuracy, precision, and recall as well as less than 3.4% of the missing rate and the over rate.

• The Journal of the Petrological Society of Korea
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• v.18 no.2
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• pp.115-135
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• 2009

We have studied general orientational characteristics of microcracks distributed in Tertiary crystalline tuff from the northeastern part of the Gyeongsang Basin. 108 sets of microcracks on horizontal surfaces of 6 rock samples from Heunghae-eup and Cheongha-myeon, Pohang-si areas were distinguished by image processing. Those microcrack sets show a distinct linear array in 38 images. Whole domain of the directional angle(${\theta}$)-frequency(N) chart for crystalline tuff can be divided into 20 domains in terms of the phases of the distribution of microcracks. From the related chart, microcrack sets show preferred orientation which are coincident with the direction of vertical common joints. Consequently, the potential for macroscopic vertical joints in a rock body can be inferred from the directional angle showing high frequency in each domain of the related chart. This joint pattern is nearly the same in Mesozoic granites from Seokmo-do, Gwanghwa-gun. From the rose diagram for orientations of microcrack in crystalline tuff, orientations of dominant sets of microcracks in terms of frequency orders reflect representative orientations of maximum principal stress acted on crystalline tuff. Meanwhile, orientations of microcracks in crystalline tuff were compared with those of open microcracks in Bulgugsa granites from the southwestern part of the Gyeongsang Basin, and vertical rift/grain planes from Mesozoic granite quarries in Korea. In regional distribution chart, the agreement of distribution pattern between above two types of microcrack sets and vertical planes suggests that microcrack systems developed in crystalline tuff probably occur regionally in Mesozoic granites in Korea.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.599-608
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• 2017

Cracks on the asphalt road surface can affect the speed of the car, the consumption of fuel, the ride quality of the road, and the durability of the road surface. Such cracks in roads can lead to very dangerous consequences for long periods of time. To prevent such risks, it is necessary to identify cracks and take appropriate action. It takes too much time and money to do it. Also, it is difficult to use expensive laser equipment vehicles for initial cost and equipment operation. In this paper, we propose an effective detection method of road surface defect using ROI (Region of Interest) setting and cany edge detection method using UAV image. The results of this study can be presented as efficient method for road surface flaw detection and maintenance using UAV. In addition, it can be used to detect cracks such as various buildings and civil engineering structures such as buildings, outer walls, large-scale storage tanks other than roads, and cost reduction effect can be expected.