• Journal of information and communication convergence engineering
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• 제6권2호
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• pp.204-206
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• 2008

In this paper, we proposed the recognition method that automatically extracts cracks from a surface image acquired by a digital camera and recognizes the directions (horizontal, vertical, -45 degree, and 45 degree) of cracks using the fuzzy single layer perceptron. We compensate an effect of light on a concrete surface image by applying the closing operation, which is one of the morphological techniques, extract the edges of cracks by Sobel masking, and binarize the image by applying the iterated binarization technique. Two times of noise reduction are applied to the binary image for effective noise elimination. After the specific regions of cracks are automatically extracted from the preprocessed image by applying Glassfire labeling algorithm to the extracted crack image, the cracks of the specific region are enlarged or reduced to $30{\times}30$ pixels and then used as input patterns to the fuzzy single layer perceptron. The experiments using concrete crack images showed that the cracks in the concrete crack images were effectively extracted and the fuzzy single layer perceptron was effective in the recognition of the extracted cracks directions.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2004년도 춘계 학술발표회 제16권1호
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• pp.716-719
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• 2004

This study proposes an algorithm for recognition of crack patterns, which includes horizontal, vertical, diagonal$(-45^{\circ})$, diagonal$(+45^{\circ})$, and random cracks, based on image processing technique and artificial neural network. A MATLAB code was developed for the proposed algorithm, and then numerical tests were performed on thirty-eight crack images to examine validity of the algorithm. Within the limited tests in the present study, the proposed algorithm was revealed as accurately recognizing the crack patterns when compared to those classified by a human expert.

• 한국건설관리학회:학술대회논문집
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• 한국건설관리학회 2004년도 제5회 정기학술발표대회 논문집
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• pp.561-564
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• 2004

국내외에서는 크랙실링 공법의 이점 및 도로면 유지보수 공사의 위험 요소를 인식하여 90년대 초반부터 크랙실링 자동화 장비 개발을 위한 연구를 진행하여 왔다. 기존 문헌 고찰과 도로면 크랙실링 자동화 장비(Automated Pavement Crack Sealer; APCS)의 실험실 및 현장 실험 결과, 도로면에 존재하는 크랙 네트워크를 자동으로 탐지하고 모델링하는 과정의 속도와 정확성을 향상시키는 것은 개발된 크랙실링 자동화 장비의 실용화를 위해 매우 중요한 요인으로 인식되었다 그러나, CCD 카메라를 통해 습득된 도로면 영상에서 크랙 네트워크를 완전 자동으로 인식하는 기술은 일반적인 영상 인식 분야에서 보다 외부 환경적인 요인으로 인해 낮은 인식률을 가지고 있다 본 연구를 통해 기존에 개발된 APCS 머신비전 알고리즘의 경우 도로면 영상의 환경 요인에 의해 발생된 문제점들을 많이 해결하였으나 실용화 단계에서 요구되는 크랙 인식률에는 도달하지 못하였다. 따라서, 본 연구의 목적은 기존 APCS 머신 비전 알고리즘의 완전 자동화 방식 크랙 탐지 및 모델링 알고리즘의 문제점을 분석하고 신경망 학습 기법을 이용한 크랙 인식 알고리즘을 개발하는 것이다.

• 한국농공학회논문집
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• 제61권3호
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• pp.55-65
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• 2019

Although crack on concrete exists from its early formation, crack requires attention as it affects stiffness of structure and can lead demolition of structure as it grows. Detecting cracks on concrete is needed to take action prior to performance degradation of structure, and deep learning can be utilized for it. In this study, transfer learning, one of the deep learning techniques, was used to detect the crack, as the amount of crack's image data was limited. Pre-trained Inception-v3 was applied as a base model for the transfer learning. Web scrapping was utilized to fetch images of concrete wall with or without crack from web. In the recognition of crack, image post-process including changing size or removing color were applied. In the visualization of crack, source images divided into 30px, 50px or 100px size were used as input data, and different numbers of input data per category were applied for each case. With the results of visualized crack image, false positive and false negative errors were examined. Highest accuracy for the recognizing crack was achieved when the source images were adjusted into 224px size under gray-scale. In visualization, the result using 50 data per category under 100px interval size showed the smallest error. With regard to the false positive error, the best result was obtained using 400 data per category, and regarding to the false negative error, the case using 50 data per category showed the best result.

• 비파괴검사학회지
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• 제10권2호
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• pp.23-31
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• 1990

Acoustic Emission(AE) signals obtained during fracture toughness test and fatigue test for nuclear pressure vessel material(SA 508 cl.3) and artificial AE signals from pencil break and ultrasonic pulser were classified using pattern recognition methods. Three different classifiers ; namely Minimum Distance Classifier, Linear Discriminant Classifier and Maximum Likelihood Classifier were used for pattern recognition. In this study, the performance of each classifier was compared. The discrimination of AE signals from cracking and crack surface rubbing was possible and the analysis for crack propagation was applicable by pattern recognition methods.

• Smart Structures and Systems
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• 제29권1호
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• pp.207-220
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• 2022

Structural health monitoring (SHM) plays a vital role in the maintenance and operation of constructions. In recent years, autonomous inspection has received considerable attention because conventional monitoring methods are inefficient and expensive to some extent. To develop autonomous inspection, a potential approach of crack identification is needed to locate defects. Therefore, this study exploits two deep learning-based segmentation models, DeepLabv3+ and Mask R-CNN, for crack segmentation because these two segmentation models can outperform other similar models on public datasets. Additionally, impacts of label quality on model performance are explored to obtain an empirical guideline on the preparation of image datasets. The influence of image cropping and label refining are also investigated, and different strategies are applied to the dataset, resulting in six alternated datasets. By conducting experiments with these datasets, the highest mean Intersection-over-Union (mIoU), 75%, is achieved by Mask R-CNN. The rise in the percentage of annotations by image cropping improves model performance while the label refining has opposite effects on the two models. As the label refining results in fewer error annotations of cracks, this modification enhances the performance of DeepLabv3+. Instead, the performance of Mask R-CNN decreases because fragmented annotations may mistake an instance as multiple instances. To sum up, both DeepLabv3+ and Mask R-CNN are capable of crack identification, and an empirical guideline on the data preparation is presented to strengthen identification successfulness via image cropping and label refining.

• 제어로봇시스템학회논문지
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• 제10권1호
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• pp.66-72
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• 2004

To assess tunnel safety, cracks in tunnel lining are measured by inspectors, who observe cracks with their naked eyes and record them. But manual inspection is slow, and measured crack data is subjective. Therefore, this study proposes inspection system fur measuring cracks in tunnel lining and providing objective crack data to be used in safety assessment. The system consists of On-vehicle system and Laboratory system. On-Vehicle system acquires image data with line CCD camera on scanning along the tunnel lining. Laboratory system extracts crack information from the acquired image using image processing. Measured crack information is crack thickness, length and orientation. To improve accuracy of crack recognition, the geometric properties and patterns of cracks in concrete structure were applied to image processing. The proposed system was verified with experiments in both laboratory environment and field environment such as subway tunnel.

• 대한전자공학회논문지SP
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• 제46권3호
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• pp.125-137
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• 2009

음향방출 기법은 작동중인 상태에서 기계 설비를 비파괴 검사할 수 있는 기법이며, 균열성장 같은 장애의 신뢰성 있는 감시를 위해서 순간적인 균열신호뿐만 아니라 동특성을 이용하는 것이 중요하다. 균열성장을 검출하기 위해 널리 사용되는 물리적 파괴 3단계는 음향방출 현상이 시간에 따라 서로 겹치는 문제점이 있어 정확한 균열성장 시간을 추정하기 어렵다. 제안한 패턴인식 기법은 오경보와 미탐지를 최소화하기 위해서 음향방출 동특성을 입력으로 사용하고, 균열성장 시간을 정확히 추정하기 위해 시간에 따른 클러스터링 기법을 사용한다. 실험결과는 제안한 패턴인식 기법이 압력의 변화에 의한 음향방출의 변화의 강인함 때문에 실용화에 효율적임을 보여준다.

• 한국소음진동공학회논문집
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• 제23권9호
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• pp.814-822
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• 2013

For the fault diagnosis of a mechanical system, pattern recognition methods have being used frequently in recent research. Hidden Markov model(HMM) and artificial neural network(ANN) are typical examples of pattern recognition methods employed for the fault diagnosis of a mechanical system. In this paper, a hybrid method that combines HMM and ANN for the fault diagnosis of a mechanical system is introduced. A rotating blade which is used for a wind turbine is employed for the fault diagnosis. Using the HMM/ANN hybrid model along with the numerical model of the rotating blade, the location and depth of a crack as well as its presence are identified. Also the effect of signal to noise ratio, crack location and crack size on the success rate of the identification is investigated.

• Smart Structures and Systems
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• 제28권6호
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• pp.779-789
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• 2021

Cracks in railway sleeper are an inevitable condition and has a significant influence on the safety of railway system. Although the technology of railway sleeper condition monitoring using machine learning (ML) models has been widely applied, the crack recognition accuracy is still in need of improvement. In this paper, a two-stage method using edge detection and convolutional neural network (CNN) is proposed to reduce the burden of computing for detecting cracks in railway sleepers with high accuracy. In the first stage, the edge detection is carried out by using the 3×3 neighborhood range algorithm to find out the possible crack areas, and a series of mathematical morphology operations are further used to eliminate the influence of noise targets to the edge detection results. In the second stage, a CNN model is employed to classify the results of edge detection. Through the analysis of abundant images of sleepers with cracks, it is proved that the cracks detected by the neighborhood range algorithm are superior to those detected by Sobel and Canny algorithms, which can be classified by proposed CNN model with high accuracy.