• Journal of the Korean Society for Nondestructive Testing
• /
• v.29 no.5
• /
• pp.493-500
• /
• 2009

An ultrasonic signal processing algorithm was developed for extracting the information of cracks generated around the keyway of a turbine rotor disk. B-scan images were obtained by using keyway specimens and an ultrasonic scan system with x-y position controller. The B-scan images were used as input images for 2-Dimensional signal processing, and the algorithm was constructed with four processing stages of pre-processing, crack candidate region detection, crack region classification and crack information extraction. It is confirmed by experiments that the developed algorithm is effective for the quantitative evaluation of cracks generated around the keyway of turbine rotor disk.

• Computers and Concrete
• /
• v.23 no.6
• /
• pp.445-457
• /
• 2019

Cracks are an important distress of concrete bridges, and may reduce the life and safety of bridges. However, the traditional manual crack detection means highly depend on the experience of inspectors. Furthermore, it is time-consuming, expensive, and often unsafe when inaccessible position of bridge is to be assessed, such as viaduct pier. To solve this question, the real-time automatic crack detecting system with unmanned aerial vehicle (UAV) become a choice. This paper designs a new automatic detection system based on real-time comprehensive image processing for bridge crack. It has small size, light weight, low power consumption and can be carried on a small UAV for real-time data acquisition and processing. The real-time comprehensive image processing algorithm used in this detection system combines the advantage of connected domain area, shape extremum, morphology and support vector data description (SVDD). The performance and validity of the proposed algorithm and system are verified. Compared with other detection method, the proposed system can effectively detect cracks with high detection accuracy and high speed. The designed system in this paper is suitable for practical engineering applications.

• Proceedings of the Optical Society of Korea Conference
• /
• 2007.07a
• /
• pp.151-152
• /
• 2007

• The Journal of the Korea Contents Association
• /
• v.11 no.12
• /
• pp.522-532
• /
• 2011

In Oriental medicine, the status of a tongue is the important indicator to diagnose the condition of internal organs in a body. A tongue diagnosis is not only convenient but also non-invasive, and therefore widely used in Oriental medicine. But tongue diagnosis has some problems that should be objective and standardized, it also exhaust the diagnosis tool that can help for oriental medicine doctor's decision-making. In this paper, to solve the this problem we propose a method that calculates the tongue geometry information for effective tongue diagnosis analysis. Our method is to extract the tongue region for using improved snake algorithm, and calculates the geometry information by using convex hull and In-painting. In experiment, our method has stable performance as 7.2% by tooth plate and 8.5% by crack in region difference ratio.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.07a
• /
• pp.667-669
• /
• 2021

본 논문에서는 벡터 기반 데이터 증강 기법(Data augmentation)을 제안하여 학습 데이터를 구축한 뒤, 이를 합성곱 신경망(Convolutional Neural Networks, CNN)으로 실제 균열과 가까운 패턴을 표현할 수 있는 프레임워크를 제안한다. 건축물의 균열은 인명 피해를 가져오는 건물 붕괴와 낙하 사고를 비롯한 큰 사고의 원인이다. 이를 인공지능으로 해결하기 위해서는 대량의 데이터 확보가 필수적이다. 하지만, 실제 균열 이미지는 복잡한 패턴을 가지고 있을 뿐만 아니라, 위험한 상황에 노출되기 때문에 대량의 데이터를 확보하기 어렵다. 이러한 데이터베이스 구축의 문제점은 인위적으로 특정 부분에 변형을 주어 데이터양을 늘리는 탄성왜곡(Elastic distortion) 기법으로 해결할 수 있지만, 본 논문에서는 이보다 향상된 균열 패턴 결과를 CNN을 활용하여 보여준다. 탄성왜곡 기법보다 CNN을 이용했을 때, 실제 균열 패턴과 유사하게 추출된 결과를 얻을 수 있었고, 일반적으로 사용되는 픽셀 기반 데이터가 아닌 벡터 기반으로 데이터 증강을 설계함으로써 균열의 변화량 측면에서 우수함을 보였다. 본 논문에서는 적은 개수의 균열 데이터를 입력으로 사용했음에도 불구하고 균열의 방향 및 패턴을 다양하게 생성하여 쉽게 균열 데이터베이스를 구축할 수 있었다. 이는 장기적으로 구조물의 안정성 평가에 이바지하여 안전사고에 대한 불안감에서 벗어나 더욱 안전하고 쾌적한 주거 환경을 조성할 것으로 기대된다.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.1-16
• /
• 2022

Despite recent breakthroughs in deep learning and computer vision fields, the pixel-wise identification of tiny objects in high-resolution images with complex disturbances remains challenging. This study proposes a modified U-net for tiny crack segmentation in real-world steel-box-girder bridges. The modified U-net adopts the common U-net framework and a novel Self-Attention-Self-Adaption (SASA) neuron as the fundamental computing element. The Self-Attention module applies softmax and gate operations to obtain the attention vector. It enables the neuron to focus on the most significant receptive fields when processing large-scale feature maps. The Self-Adaption module consists of a multiplayer perceptron subnet and achieves deeper feature extraction inside a single neuron. For data augmentation, a grid-based crack random elastic deformation (CRED) algorithm is designed to enrich the diversities and irregular shapes of distributed cracks. Grid-based uniform control nodes are first set on both input images and binary labels, random offsets are then employed on these control nodes, and bilinear interpolation is performed for the rest pixels. The proposed SASA neuron and CRED algorithm are simultaneously deployed to train the modified U-net. 200 raw images with a high resolution of 4928 × 3264 are collected, 160 for training and the rest 40 for the test. 512 × 512 patches are generated from the original images by a sliding window with an overlap of 256 as inputs. Results show that the average IoU between the recognized and ground-truth cracks reaches 0.409, which is 29.8% higher than the regular U-net. A five-fold cross-validation study is performed to verify that the proposed method is robust to different training and test images. Ablation experiments further demonstrate the effectiveness of the proposed SASA neuron and CRED algorithm. Promotions of the average IoU individually utilizing the SASA and CRED module add up to the final promotion of the full model, indicating that the SASA and CRED modules contribute to the different stages of model and data in the training process.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.7
• /
• pp.1575-1581
• /
• 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 the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.23 no.1
• /
• pp.9-17
• /
• 2005

The objective of the study is to extract information about the road surfaces to be studied by analyzing asphalt concrete-paved road surface images photographed with a digital video camera. To analyze the accuracy of road surface information gained using a digital imagery processing method, it was compared and analyzed with the outcomes of control surveying. As a result, an average error of 0.0427 m in the X-axis direction, that of 0.0527 m in the Y-axis direction, and that of 0.1539 m in the Z-axis direction were found, good enough for mapping at a scale of 1:1,000 or less and GIS data. Besides, information on road surface assessment factors such as crack ratio, the amount of rutting and profile index was gained by analyzing processed digital imagery. This information made it possible to conduct road surface assessment by generating PSI and MCI. As quality digital image information has been gathered from roads and stored, important fundamental data on PMS (Pavement Management System) will become available in the future.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.912-919
• /
• 2022

The study is an image-based assessment that uses image processing techniques to determine the condition of concrete with surface cracks. The preparations of the dataset include resizing and image filtering to ensure statistical homogeneity and noise reduction. The image dataset is then segmented, making it more suited for extracting important features and easier to evaluate. The image is transformed into grayscale which removes the hue and saturation but retains the luminance. To create a clean edge map, the edge detection process is utilized to extract the major edge features of the image. The Otsu method is used to minimize intraclass variation between black and white pixels. Additionally, the median filter was employed to reduce noise while keeping the borders of the image. Image processing techniques are used to enhance the significant features of the concrete image, especially the defects. In this study, the tonal zones of the histogram and its properties are used to analyze the condition of the concrete. By examining the histogram, the viewer will be able to determine the information on the image through the number of pixels associated and each tonal characteristic on a graph. The features of the five tonal zones of the histogram which implies the qualities of the concrete image may be evaluated based on the quality of the contrast, brightness, highlights, shadow spikes, or the condition of the shadow region that corresponds to the foreground.

• Journal of information and communication convergence engineering
• /
• v.5 no.2
• /
• pp.126-130
• /
• 2007

This paper proposes a method that automatically extracts characteristics of cracks such as length, thickness and direction, etc., from a concrete surface image with image processing techniques. This paper, first, uses the closing morphologic operation to adjust the effect of light extending over the whole concrete surface image. After applying the high-pass filtering operation to sharpen boundaries of cracks, we classify intensity values of the image into 8 groups and remove intensity values belong to the highest frequency group among them for the removal of background. Then, we binarize the preprocessed image. The auxiliary lines used to measure cracks of concrete surface are removed from the binarized image with position information extracted by the histogram operation. Then, cracks broken by the removal of background are extended to reconstruct an original crack with the $5{\times}5$ masking operation. We remove unnecessary information by applying three types of noise removal operations successively and extracts areas of cracks from the binarized image. At last, the opening morphologic operation is applied to compensate extracted cracks and characteristics of cracks are measured on the compensated ones. Experiments using real images of concrete surface showed that the proposed method extracts cracks well and precisely measures characteristics of cracks.