Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete Structures

The damage investigation and inspection methods for infrastructures performed in small-scale (type III) facilities usually involve a visual examination by an inspector using surveying tools (e.g., cracking, crack microscope, etc.) in the field. These methods can interfere with the subjectivity of the inspector, which may reduce the objectivity and reliability of the record. Therefore, a new image analysis technique is needed to automatically detect cracks and analyze the characteristics of the cracks objectively. In this study, an image analysis technique using deep learning is developed to detect cracks and analyze characteristics (e.g., length, and width) in images for small-scale facilities. Three stages of image processing pipeline are proposed to obtain crack detection and its characteristics. In the first and second stages, two-dimensional convolutional neural networks are used for crack image detection (e.g., classification and segmentation). Based on convolution neural network for the detection, hierarchical feature learning architecture is applied into our deep learning network. After deep learning-based detection, in the third stage, thinning and tracking algorithms are applied to analyze length and width of crack in the image. The performance of the proposed method was tested using various crack images with label and the results showed good performance of crack detection and its measurement.

[1]  Paulo Lobato Correia,et al.  Automatic Road Crack Detection and Characterization , 2013, IEEE Transactions on Intelligent Transportation Systems.

[2]  Paul Fieguth,et al.  Automated detection of cracks in buried concrete pipe images , 2006 .

[3]  Hui Li,et al.  Identification framework for cracks on a steel structure surface by a restricted Boltzmann machines algorithm based on consumer‐grade camera images , 2018 .

[4]  Paul Fieguth,et al.  Segmentation of buried concrete pipe images , 2006 .

[5]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[6]  Hamid Jafarkhani,et al.  A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI , 2015, Medical Image Anal..

[7]  N. Kanopoulos,et al.  Design of an image edge detection filter using the Sobel operator , 1988 .

[8]  Jian Zhang,et al.  Zernike‐moment measurement of thin‐crack width in images enabled by dual‐scale deep learning , 2018, Comput. Aided Civ. Infrastructure Eng..

[9]  Matthew Lai,et al.  Deep Learning for Medical Image Segmentation , 2015, Deep Learning Applications in Medical Imaging.

[10]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[11]  Xiaochun Luo,et al.  Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network , 2018, Comput. Aided Civ. Infrastructure Eng..

[12]  Bugao Xu,et al.  Automatic inspection of pavement cracking distress , 2006, J. Electronic Imaging.

[13]  Xuefeng Zhao,et al.  Image-Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique , 2019, Advances in Civil Engineering.

[14]  Shuji Hashimoto,et al.  Image‐Based Crack Detection for Real Concrete Surfaces , 2008 .

[15]  Kwang-Baek Kim,et al.  Detection of Concrete Surface Cracks using Fuzzy Techniques , 2010 .

[16]  Fatih Kucuksubasi,et al.  Transfer Learning-Based Crack Detection by Autonomous UAVs , 2018, Proceedings of the 35th International Symposium on Automation and Robotics in Construction (ISARC).

[17]  Yong Fan,et al.  A deep learning model integrating FCNNs and CRFs for brain tumor segmentation , 2017, Medical Image Anal..

[18]  Shivprakash Iyer,et al.  A robust approach for automatic detection and segmentation of cracks in underground pipeline images , 2005, Image Vis. Comput..

[19]  Ignacio Parra,et al.  Adaptive Road Crack Detection System by Pavement Classification , 2011, Sensors.

[20]  Kasthurirangan Gopalakrishnan,et al.  CRACK DAMAGE DETECTION IN UNMANNED AERIAL VEHICLE IMAGES OF CIVIL INFRASTRUCTURE USING PRE-TRAINED DEEP LEARNING MODEL , 2018 .

[21]  Asif Khan,et al.  Improved Crack Detection and Recognition Based on Convolutional Neural Network , 2019, Modelling and Simulation in Engineering.

[22]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[23]  Tomoyuki Yamaguchi,et al.  Practical image measurement of crack width for real concrete structure , 2009 .

[24]  Eric Feulvarch,et al.  XFEM investigation of a crack path in residual stresses resulting from quenching , 2013 .

[25]  Sébastien Ourselin,et al.  On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task , 2017, IPMI.

[26]  Young-Jin Cha,et al.  Vision-Based Concrete Crack Detection Using a Convolutional Neural Network , 2017 .