• Title/Summary/Keyword: crack network

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Crack Detection in Tunnel Using Convolutional Encoder-Decoder Network (컨볼루셔널 인코더-디코더 네트워크를 이용한 터널에서의 균열 검출)

  • Han, Bok Gyu;Yang, Hyeon Seok;Lee, Jong Min;Moon, Young Shik
    • Journal of the Institute of Electronics and Information Engineers
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    • v.54 no.6
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    • pp.80-89
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    • 2017
  • The classical approaches to detect cracks are performed by experienced inspection professionals by annotating the crack patterns manually. Because of each inspector's personal subjective experience, it is hard to guarantee objectiveness. To solve this issue, automated crack detection methods have been proposed however the methods are sensitive to image noise. Depending on the quality of image obtained, the image noise affect overall performance. In this paper, we propose crack detection method using a convolutional encoder-decoder network to overcome these weaknesses. Performance of which is significantly improved in terms of the recall, precision rate and F-measure than the previous methods.

A deep and multiscale network for pavement crack detection based on function-specific modules

  • Guolong Wang;Kelvin C.P. Wang;Allen A. Zhang;Guangwei Yang
    • 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.

Pavement Crack Detection and Segmentation Based on Deep Neural Network

  • Nguyen, Huy Toan;Yu, Gwang Hyun;Na, Seung You;Kim, Jin Young;Seo, Kyung Sik
    • The Journal of Korean Institute of Information Technology
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    • v.17 no.9
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    • pp.99-112
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    • 2019
  • Cracks on pavement surfaces are critical signs and symptoms of the degradation of pavement structures. Image-based pavement crack detection is a challenging problem due to the intensity inhomogeneity, topology complexity, low contrast, and noisy texture background. In this paper, we address the problem of pavement crack detection and segmentation at pixel-level based on a Deep Neural Network (DNN) using gray-scale images. We propose a novel DNN architecture which contains a modified U-net network and a high-level features network. An important contribution of this work is the combination of these networks afforded through the fusion layer. To the best of our knowledge, this is the first paper introducing this combination for pavement crack segmentation and detection problem. The system performance of crack detection and segmentation is enhanced dramatically by using our novel architecture. We thoroughly implement and evaluate our proposed system on two open data sets: the Crack Forest Dataset (CFD) and the AigleRN dataset. Experimental results demonstrate that our system outperforms eight state-of-the-art methods on the same data sets.

The Prediction Modelling on the Stress Intensity Factor of Two Dimensional Elastic Crack Emanating from the Hole Using Neural Network and Boundary element Method (신경회로망과 경계요소법을 이용한 원공에서 파생하는 2차원 탄성균열의 응력세기계수 예측 모델링)

  • Yun, In-Sik;Yi, Won
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.25 no.3
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    • pp.353-361
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    • 2001
  • Recently the boundary element method has been developed swiftly. The boundary element method is an efficient and accurate means for analysis of two dimensional elastic crack problems. This paper is concerned with the evaluation and the prediction of the stress intensity factor(SIF) for the crack emanating from the circular hole using boundary element method-neural network. The SIF of the crack emanating from the hole was calculated by using boundary element method. Neural network is used to evaluate and to predict SIF from the results of boundary element method. The organized neural network system (structure of four processing element) was learned with the accuracy 99%. The learned neural network system could be evaluated and predicted with the accuracy of 83.3% and 71.4% (in cases of SIF and virtual SIF). Thus the proposed boundary element method-neural network is very useful to estimate the SIF.

A Study on Development of Automatically Recognizable System in Types of Welding Flaws by Neural Network (신경회로망에 의한 용접 결함 종류의 정량적인 자동인식 시스템 개발에 관한 연구)

  • 김재열
    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.6 no.1
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    • pp.27-33
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    • 1997
  • A neural network approach has been developed to determine the depth of a surface breaking crack in a steel plate from ultrasonic backscattering data. The network is trained by the use of feedforward three-layered network together with a back-scattering algorithm for error correction. The signal used for crack insonification is a mode converted 70$^{\circ}$transverse wave. A numerical analysis of back scattered field is carried out based on elastic wave theory, by the use of the boundary element method. The numerical data are calibrated by comparison with experimental data. The numerical analysis provides synthetic data for the training of the network. The training data have been calculated for cracks with specified increments of the crack depth. The performance of the network has been tested on other synthetic data and experimental data which are different from the training data.

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Vibration-Based Damage Detection Method for Tower Structure (타워 구조물의 진동기반 결함탐지기법)

  • Lee, Jong-Won;Kim, Sang-Ryul;Kim, Bong-Ki
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2013.10a
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    • pp.320-324
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    • 2013
  • A crack identification method using an equivalent bending stiffness for cracked beam and committee of neural networks is presented. The equivalent bending stiffness is constructed based on an energy method for a straight thin-walled pipe, which has a through-the-thickness crack, subjected to bending. Several numerical analysis for a steel cantilever pipe using the equivalent bending stiffness are carried out to extract the natural frequencies and mode shapes of the cracked beam. The extracted modal properties are used in constructing a training patterns of a neural network. The input to the neural network consists of the modal properties and the output is composed of the crack location and size. Multiple neural networks are constructed and each individual network is trained independently with different initial synaptic weights. Then, the estimated crack locations and sizes from different neural networks are averaged. Experimental crack detection is carried out for 3 damage cases using the proposed method, and the identified crack locations and sizes agree reasonably well with the exact values.

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Crack Identification Using Neuro-Fuzzy-Evolutionary Technique

  • Shim, Mun-Bo;Suh, Myung-Won
    • Journal of Mechanical Science and Technology
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    • v.16 no.4
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    • pp.454-467
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    • 2002
  • It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. Toidentifythelocation and depth of a crack in a structure, a method is presented in this paper which uses neuro-fuzzy-evolutionary technique, that is, Adaptive-Network-based Fuzzy Inference System (ANFIS) solved via hybrid learning algorithm (the back-propagation gradient descent and the least-squares method) and Continuous Evolutionary Algorithms (CEAs) solving sir ale objective optimization problems with a continuous function and continuous search space efficiently are unified. With this ANFIS and CEAs, it is possible to formulate the inverse problem. ANFIS is used to obtain the input(the location and depth of a crack) - output(the structural Eigenfrequencies) relation of the structural system. CEAs are used to identify the crack location and depth by minimizing the difference from the measured frequencies. We have tried this new idea on beam structures and the results are promising.

Crack detection method for step-changed non-uniform beams using natural frequencies

  • Lee, Jong-Won
    • Smart Structures and Systems
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    • v.30 no.2
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    • pp.173-181
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    • 2022
  • The current paper presents a technique to detect crack in non-uniform cantilever-type pipe beams, that have step changes in the properties of their cross sections, restrained by a translational and rotational spring with a tip mass at the free end. An equation for estimating the natural frequencies for the non-uniform beams is derived using the boundary and continuity conditions, and an equivalent bending stiffness for cracked beam is applied to calculate the natural frequencies of the cracked beam. An experimental study for a step-changed non-uniform cantilever-type pipe beam restrained by bolts with a tip mass is carried out to verify the proposed method. The translational and rotational spring constants are updated using the neural network technique to the results of the experiment for intact case in order to establish a baseline model for the subsequent crack detection. Then, several numerical simulations for the specimen are carried out using the derived equation for estimating the natural frequencies of the cracked beam to construct a set of training patterns of a neural network. The crack locations and sizes are identified using the trained neural network for the 5 damage cases. It is found that the crack locations and sizes are reasonably well estimated from a practical point of view. And it is considered that the usefulness of the proposed method for structural health monitoring of the step-changed non-uniform cantilever-type pipe beam-like structures elastically restrained in the ground and have a tip mass at the free end could be verified.

A Technique for Pattern Recognition of Concrete Surface Cracks (콘크리트 표면 균열 패턴인식 기법 개발)

  • Lee Bang-Yeon;Park Yon-Dong;Kim Jin-Keun
    • Journal of the Korea Concrete Institute
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    • v.17 no.3 s.87
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    • pp.369-374
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    • 2005
  • This study proposes a technique for the 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 image processing algorithm and artificial neural network. Features were determined using total projection technique, and the structure(no. of layers and hidden neurons) and weight of artificial neural network were determined by learning from artificial crack images. In this process, we adopted Bayesian regularization technique as a generalization method to eliminate overfitting Problem. 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.

Crack Identification Using Hybrid Neuro-Genetic Technique (인공신경망 기법과 유전자 기법을 혼합한 결함인식 연구)

  • Suh, Myung-Won;Shim, Mun-Bo
    • Journal of the Korean Society for Precision Engineering
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    • v.16 no.11
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    • pp.158-165
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    • 1999
  • It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses hybrid neuro-genetic technique. Feed-forward multilayer neural networks trained by back-propagation are used to learn the input)the location and dept of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this neural network and genetic algorithm, it is possible to formulate the inverse problem. Neural network training algorithm is the back propagation algorithm with the momentum method to attain stable convergence in the training process and with the adaptive learning rate method to speed up convergence. Finally, genetic algorithm is used to fine the minimum square error.

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