Foreground–background separation technique for crack detection

Current level-2 condition assessment methods for critical infrastructure assets mostly rely on human visual investigation of visible damages and patterns at the structure surface, which can be a costly, time-consuming, and subjective exercise in reality. In this article, a novel method for crack detection is proposed via salient structure extraction from textured background. This method first extracts strong edges and distinguishes them from strong textures in a local neighborhood. Then, the spatial distribution of texture features is estimated to detect cracks as salient structures that are not widely spread across the whole image. The outputs from these two key steps are fused to calculate the final structure saliency map for generation of the crack masks. This method was validated on a data set with 704 images and the outcome revealed an average f-measure of 75% in detecting the concrete cracks that is significantly higher than two other baseline methods.

[1]  Liming Tang,et al.  Multiscale Texture Extraction with Hierarchical (BV,Gp,L2) Decomposition , 2012, Journal of Mathematical Imaging and Vision.

[2]  Ikhlas Abdel-Qader,et al.  ANALYSIS OF EDGE-DETECTION TECHNIQUES FOR CRACK IDENTIFICATION IN BRIDGES , 2003 .

[3]  Shuji Hashimoto,et al.  Fast crack detection method for large-size concrete surface images using percolation-based image processing , 2010, Machine Vision and Applications.

[4]  Weihua Sheng,et al.  Developing a crack inspection robot for bridge maintenance , 2011, 2011 IEEE International Conference on Robotics and Automation.

[5]  Khalid M. Mosalam,et al.  Deep Transfer Learning for Image‐Based Structural Damage Recognition , 2018, Comput. Aided Civ. Infrastructure Eng..

[6]  Chang-Soo Han,et al.  Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel , 2007 .

[7]  Kristin J. Dana,et al.  Computer-vision based crack detection and analysis , 2012, Smart Structures.

[8]  Nii O. Attoh-Okine,et al.  Evaluating Pavement Cracks with Bidimensional Empirical Mode Decomposition , 2008, EURASIP J. Adv. Signal Process..

[9]  Hyung Jin Lim,et al.  Fatigue crack detection using structural nonlinearity reflected on linear ultrasonic features , 2015 .

[10]  Carlos Eduardo Pereira,et al.  Embedded Image Processing Systems for Automatic Recognition of Cracks using UAVs , 2015 .

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

[12]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[13]  Jian Zhou,et al.  Wavelet-based pavement distress detection and evaluation , 2003 .

[14]  Jérôme Idier,et al.  Automatic Crack Detection on Two-Dimensional Pavement Images: An Algorithm Based on Minimal Path Selection , 2016, IEEE transactions on intelligent transportation systems (Print).

[15]  Jian Li,et al.  Vision‐Based Fatigue Crack Detection of Steel Structures Using Video Feature Tracking , 2018, Comput. Aided Civ. Infrastructure Eng..

[16]  Li Xu,et al.  Structure extraction from texture via relative total variation , 2012, ACM Trans. Graph..

[17]  Dietmar Meinel,et al.  Comparison of Crack Detection Methods for Analyzing Damage Processes in Concrete with Computed Tomography , 2011 .

[18]  Yang Liu,et al.  Automated Pixel‐Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep‐Learning Network , 2017, Comput. Aided Civ. Infrastructure Eng..

[19]  Manuel Avila,et al.  Automatic detection and classification of defect on road pavement using anisotropy measure , 2009, 2009 17th European Signal Processing Conference.

[20]  Yashon O. Ouma,et al.  Wavelet-morphology based detection of incipient linear cracks in asphalt pavements from RGB camera imagery and classification using circular Radon transform , 2016, Adv. Eng. Informatics.

[21]  Jie Gong,et al.  Automated Analysis of Mobile LiDAR Data for Component‐Level Damage Assessment of Building Structures during Large Coastal Storm Events , 2018, Comput. Aided Civ. Infrastructure Eng..

[22]  Nanning Zheng,et al.  Learning to Detect a Salient Object , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Ting-Hua Yi,et al.  Blind Modal Identification in Frequency Domain Using Independent Component Analysis for High Damping Structures with Classical Damping , 2018, Comput. Aided Civ. Infrastructure Eng..

[24]  Jerome P. Lynch,et al.  Structural health monitoring: technological advances to practical implementations [scanning the issue] , 2016, Proc. IEEE.

[25]  Yicheng Li,et al.  A Fast Detection Method via Region‐Based Fully Convolutional Neural Networks for Shield Tunnel Lining Defects , 2018, Comput. Aided Civ. Infrastructure Eng..

[26]  H. Adeli,et al.  Synchrosqueezed wavelet transform-fractality model for locating, detecting, and quantifying damage in smart highrise building structures , 2015 .

[27]  Hui-li Zhao,et al.  Improvement of canny algorithm based on pavement edge detection , 2010, 2010 3rd International Congress on Image and Signal Processing.

[28]  Jun Wang,et al.  Defining a conceptual framework for the integration of modelling and advanced imaging for improving the reliability and efficiency of bridge assessments , 2016 .

[29]  Kelvin C. P. Wang,et al.  Hybrid Procedure for Automated Detection of Cracking with 3D Pavement Data , 2016, J. Comput. Civ. Eng..

[30]  Stephen R. Lockley,et al.  BIM in facilities management applications: a case study of a large university complex , 2015 .

[31]  Hojjat Adeli,et al.  Feature Extraction for Traffic Incident Detection Using Wavelet Transform and Linear Discriminant Analysis , 2000 .

[32]  Peggy Subirats,et al.  Automation of Pavement Surface Crack Detection using the Continuous Wavelet Transform , 2006, 2006 International Conference on Image Processing.

[33]  Tara C. Hutchinson,et al.  Improved image analysis for evaluating concrete damage , 2006 .

[34]  Kelvin C. P. Wang,et al.  Pavement Crack Width Measurement Based on Laplace's Equation for Continuity and Unambiguity , 2018, Comput. Aided Civ. Infrastructure Eng..

[35]  Hojjat Adeli,et al.  A novel machine learning‐based algorithm to detect damage in high‐rise building structures , 2017 .

[36]  James H. Garrett,et al.  Automated defect detection for sewer pipeline inspection and condition assessment , 2009 .

[37]  Sue McNeil,et al.  BRIDGE MANAGEMENT AND INSPECTION DATA: LEVERAGING THE DATA AND IDENTIFYING THE GAPS , 1999 .

[38]  Boguslaw Cyganek,et al.  Image recognition with deep neural networks in presence of noise - Dealing with and taking advantage of distortions , 2017, Integr. Comput. Aided Eng..

[39]  Ting-Hua Yi,et al.  Blind Modal Identification Using Limited Sensors through Modified Sparse Component Analysis by Time-Frequency Method , 2018, Comput. Aided Civ. Infrastructure Eng..

[40]  Umapada Pal,et al.  Automatic Bridge Crack Detection - A Texture Analysis-Based Approach , 2014, ANNPR.

[41]  David Lattanzi,et al.  Robust Automated Concrete Damage Detection Algorithms for Field Applications , 2014, J. Comput. Civ. Eng..

[42]  Manuel Avila,et al.  2D image based road pavement crack detection by calculating minimal paths and dynamic programming , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[43]  Eduardo Zalama Casanova,et al.  Enhanced automatic detection of road surface cracks by combining 2D/3D image processing techniques , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[44]  Benjamin A. Graybeal,et al.  Routine Highway Bridge Inspection Condition Documentation Accuracy and Reliability , 2004 .

[45]  Wei Xu,et al.  Pavement crack detection based on saliency and statistical features , 2013, 2013 IEEE International Conference on Image Processing.

[46]  Cong Zhou,et al.  Damage Identification for Hysteretic Structures Using a Mode Decomposition Method , 2018, Comput. Aided Civ. Infrastructure Eng..

[47]  Hojjat Adeli,et al.  Supervised Deep Restricted Boltzmann Machine for Estimation of Concrete , 2017 .

[48]  Hojjat Adeli,et al.  A novel unsupervised deep learning model for global and local health condition assessment of structures , 2018 .

[49]  Benjamin A. Graybeal,et al.  RELIABILITY OF VISUAL INSPECTION FOR HIGHWAY BRIDGES, VOLUME I: FINAL REPORT , 2001 .

[50]  Kristin J. Dana,et al.  Automated Crack Detection on Concrete Bridges , 2016, IEEE Transactions on Automation Science and Engineering.

[51]  Sherif Yehia,et al.  PCA-Based algorithm for unsupervised bridge crack detection , 2006, Adv. Eng. Softw..

[52]  Leonardo Franco,et al.  Layer multiplexing FPGA implementation for deep back-propagation learning , 2017, Integr. Comput. Aided Eng..

[53]  Lu Deng,et al.  State-of-the-Art Review on the Causes and Mechanisms of Bridge Collapse , 2016 .

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

[55]  Andrzej Rybarczyk,et al.  Asphalt surfaced pavement cracks detection based on histograms of oriented gradients , 2015, 2015 22nd International Conference Mixed Design of Integrated Circuits & Systems (MIXDES).

[56]  Oral Büyüköztürk,et al.  Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks , 2017, Comput. Aided Civ. Infrastructure Eng..

[57]  Yimin D. Zhang,et al.  Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).

[58]  Yi-Zhou Lin,et al.  Structural Damage Detection with Automatic Feature‐Extraction through Deep Learning , 2017, Comput. Aided Civ. Infrastructure Eng..