• Smart Structures and Systems
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• v.33 no.2
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• pp.105-118
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• 2024

In order to realize tiny bridge crack discovery by UAV-based machine vision, a novel method combining deep learning and tensor voting is proposed. Firstly, the grid images of crack are detected and descripted based on SE-ResNet50 to generate feature points. Then, the probability significance map of crack image is calculated by tensor voting with feature points, which can define the direction and region of crack. Further, the crack detection anchor box is formed by non-maximum suppression from the probability significance map, which can improve the robustness of tiny crack detection. Finally, a case study is carried out to demonstrate the effectiveness of the proposed method in the Xiangjiang-River bridge inspection. Compared with the original tensor voting algorithm, the proposed method has higher accuracy in the situation of only 1-2 pixels width crack and the existence of edge blur, crack discontinuity, which is suitable for UAV-based bridge crack discovery.

• Journal of Conservation Science
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• v.27 no.3
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• pp.251-260
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• 2011

This study focus on the suggestion of standard legend, the process system on making method of deterioration map, the development of crack index (CI), and the evaluation techniques of surface and 3D deterioration rate for stone cultural heritage. The standard legends of deterioration forms were made using a common graphic program after crack, blistering, scaling, break-out, granular disintegration, and perforation were subdivided. The deterioration map improved accuracy and reliability on deterioration range using 3D digital restoration and high resolution photograph mapping technique. Also, quantitative deterioration evaluation of stone cultural heritage was carried out developing the crack index, and the 3D deterioration rate of a break-out part was calculated by virtual restoration modeling. As a quantitative deterioration evaluation of Magoksa Temple stone pagoda based on the results described above, the north face showed high deterioration rate of bursting crack (1.70), hair crack (1.34), scaling (20.2%) and break out (13.0%), and the 3D deterioration rate of first roof stone was 6.7%.

• Journal of Industrial Technology
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• v.28 no.A
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• pp.89-96
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• 2008

An increase in the amount of cracking in repaired concrete bridge decks using VES-LMC(Very Early Strength - Latex Modified Concrete ; below VES-LMC) has been noticed by Yun et al(1). Literature indicates that indeed many concrete bridge decks develop transverse cracking, most developing at early ages(3~7 days), many right after construction. The purpose of this study was to establish prevention of map, transverse and longitudinal cracking in VES-LMC and to provide a control methods for minimizing the occurrence of cracks. The proposed prevention against map and transverse cracking was verified by field applications. VES cement was modified, the unit cement contents was reduced into $360kg/m^3$ from $390kg/m^3$, the maximum size of coarse aggregate was increase into 19mm from 13mm, wire mesh and steel fibers were incorporated in concrete mixture. A series of variable combinations were attempted. As a results, the proposed prevention against map and transverse cracking was verified because no crack were occurred until 90 days after overlay.

• Journal of the Korea institute for structural maintenance and inspection
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• v.13 no.3 s.55
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• pp.173-180
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• 2009

Very-early strength latex-modified concrete(below ; VES-LMC) was developed for possible early-opening-to-traffic after overlay of bridge deck concrete. The purpose of this study is to analyze the cause of map, transverse and longitudinal cracking in VES-LMC and to provide a control method for minimizing occurrence of cracking. The proposed prevention method against map and transverse cracking was verified by field data. VES cement was modified as the unit cement content was reduced from 390kg/$m^3$ to 360kg/$m^3$. The maximum size of coarse aggregate was increased from 13mm to 19mm. The wire mesh and steel fiber were adopted in concrete mixture. From the results, the proposed prevention method against map and transverse cracking was verified since structural cracking was not occurred until 3 years after overlay.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.12 no.2
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• pp.65-70
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• 2003

In this study, researchers developed the estimative algorithm for artificial defect in semiconductor packages and performed it by pattern recognition technology. For this purpose, the estimative algorithm was included that researchers made software with MATLAB. The software consists of some procedures including ultrasonic image acquisition, equalization filtering, Self-Organizing Map and Backpropagation Neural Network. Self-organizing Map and Backpropagation Neural Network are belong to methods of Neural Networks. And the pattern recognition technology has applied to classify three kinds of detective patterns in semiconductor packages : Crack, Delamination and Normal. According to the results, we were confirmed that estimative algerian was provided the recognition rates of 75.7% (for Crack) and 83.4% (for Delamination) and 87.2 % (for Normal).

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.36 no.3
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• pp.113-120
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• 2020

The purpose of this study is to compare the models of Deep Learning-based Convolution Neural Network(CNN) for concrete crack detection. The comparison models are AlexNet, GoogLeNet, VGG16, VGG19, ResNet-18, ResNet-50, ResNet-101, and SqueezeNet which won ImageNet Large Scale Visual Recognition Challenge(ILSVRC). To train, validate and test these models, we constructed 3000 training data and 12000 validation data with 256×256 pixel resolution consisting of cracked and non-cracked images, and constructed 5 test data with 4160×3120 pixel resolution consisting of concrete images with crack. In order to increase the efficiency of the training, transfer learning was performed by taking the weight from the pre-trained network supported by MATLAB. From the trained network, the validation data is classified into crack image and non-crack image, yielding True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), and 6 performance indicators, False Negative Rate (FNR), False Positive Rate (FPR), Error Rate, Recall, Precision, Accuracy were calculated. The test image was scanned twice with a sliding window of 256×256 pixel resolution to classify the cracks, resulting in a crack map. From the comparison of the performance indicators and the crack map, it was concluded that VGG16 and VGG19 were the most suitable for detecting concrete cracks.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.91-96
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• 2005

In this study, researchers developed the estimative algorithm for artificial defects in semiconductor packages and performed it by pattern recognition technology. For this purpose, the estimative algorithm was included that researchers made software with MATLAB. The software consists of some procedures including ultrasonic image acquisition, equalization filtering, Self-Organizing Map and Backpropagation Neural Network. Self-Organizing Map and Backpropagation Neural Network are belong to methods of Neural Networks. And the pattern recognition technology has applied to classify three kinds of detective patterns in semiconductor packages: Crack, Delamination and Normal. According to the results, we were confirmed that estimative algorithm was provided the recognition rates of $75.7\%$ (for Crack) and $83_4\%$ (for Delamination) and $87.2\%$ (for Normal).

• Structural Monitoring and Maintenance
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• v.1 no.3
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• pp.309-322
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• 2014

Detection of fatigue cracks at an early stage of their development is important in structural health monitoring. The breathing of cracks in a structure generates higher harmonic components of the exciting frequency in the frequency spectrum. Previously, the residual operational deflection shape (R-ODS) method was successfully applied to beams with a single crack. The method is based on the ODSs at the exciting frequency and its higher harmonic components which consider both amplitude and phase information of responses to map the deflection pattern of structures. Although the R-ODS method shows the location of a single crack clearly, its identification for the location of multiple cracks in a structure is not always obvious. Therefore, an improvement to the R-ODS method is presented here to make the identification process distinct for the beams with multiple cracks. Numerical and experimental examples are utilised to investigate the effectiveness of the improved method.

• Smart Structures and Systems
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• v.32 no.3
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• pp.135-151
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• 2023

Using 3D asphalt pavement surface data, a deep and multiscale network named CrackNet-M is proposed in this paper for pixel-level crack detection for improvements in both accuracy and robustness. The CrackNet-M consists of four function-specific architectural modules: a central branch net (CBN), a crack map enhancement (CME) module, three pooling feature pyramids (PFP), and an output layer. The CBN maintains crack boundaries using no pooling reductions throughout all convolutional layers. The CME applies a pooling layer to enhance potential thin cracks for better continuity, consuming no data loss and attenuation when working jointly with CBN. The PFP modules implement direct down-sampling and pyramidal up-sampling with multiscale contexts specifically for the detection of thick cracks and exclusion of non-crack patterns. Finally, the output layer is optimized with a skip layer supervision technique proposed to further improve the network performance. Compared with traditional supervisions, the skip layer supervision brings about not only significant performance gains with respect to both accuracy and robustness but a faster convergence rate. CrackNet-M was trained on a total of 2,500 pixel-wise annotated 3D pavement images and finely scaled with another 200 images with full considerations on accuracy and efficiency. CrackNet-M can potentially achieve crack detection in real-time with a processing speed of 40 ms/image. The experimental results on 500 testing images demonstrate that CrackNet-M can effectively detect both thick and thin cracks from various pavement surfaces with a high level of Precision (94.28%), Recall (93.89%), and F-measure (94.04%). In addition, the proposed CrackNet-M compares favorably to other well-developed networks with respect to the detection of thin cracks as well as the removal of shoulder drop-offs.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.2030-2039
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• 2001

Recently, various inpipe inspection robots are developed and its effective values are increased in industrial use. However, it is so difficult to make a inpipe inspection robot system which has flexible mobility and accuracy of inspection in pipelines. Especially, it is very important to know the exact crack position. In this paper, we are to present a lately developed inpipe inspection robot system which can resolve the above Problems. The robot is configured as an articulated structure like a snake. Two active driving vehicles are located in front and rear of the inspection robot respectively and passive modules such as a nondestructive testing module and a control module are chained between the active vehicles. Special feature of the robot system is a ground interface, which is able to show informations of robot and pipelines. By using this, so called virtual map in this paper, user is able to know the pipelines'feature and crack position.