Image Based Techniques for Crack Detection, Classification and Quantification in Asphalt Pavement: A Review

Pavement condition information is a significant component in Pavement Management Systems. The labeling and quantification of the type, severity, and extent of surface cracking is a challenging area for weighing the asphalt pavements. This paper presents a widespread review on various platform and image processing approaches for asphalt surface interpretation. The main part of this study presents a comprehensive combination of the state of the art in image processing based on crack interpretation related to asphalt pavements. An attempt is made to study the existing methodologies from different points of views accompanied by extensive comparisons on three stages of methods—distress detection, classification, and quantification to facilitate further research studies. This paper presents a survey of the developed pavement inspection systems up to date. Additionally, emerging and evolution technologies considered to automate the processes are discussed.

[1]  Juan-Zi Li,et al.  A multi-objective evolutionary algorithm for feature selection based on mutual information with a new redundancy measure , 2015, Inf. Sci..

[2]  Michel Verleysen,et al.  Mutual information-based feature selection for multilabel classification , 2013, Neurocomputing.

[3]  Ouen Pinngern,et al.  Feature subset selection wrapper based on mutual information and rough sets , 2012, Expert Syst. Appl..

[4]  Jung Gon Sung,et al.  Development of Laser Pavement Image Processing System to Enhance Existing Automated Pavement Distress Detection Process , 2007 .

[5]  Jindong Tan,et al.  A novel autonomous self-assembly distributed swarm flying robot , 2013 .

[6]  Hans W. Griepentrog,et al.  Country road and field surface profiles acquisition, modelling and synthetic realisation for evaluating fatigue life of agricultural machinery , 2016 .

[7]  Kelvin C. P. Wang.  Designs and Implementations of Automated Systems for Pavement Surface Distress Survey , 2000 .

[8]  Gonzalo R. Arce,et al.  Multiresolution Information Mining for Pavement Crack Image Analysis , 2012 .

[9]  S. Chambon,et al.  Automatic Road Pavement Assessment with Image Processing: Review and Comparison , 2011 .

[10]  Shi Su-ming.  Research on Asphalt Pavement Surface Distress Image Feature Extraction Method , 2003 .

[11]  Qingquan Li,et al.  FoSA: F* Seed-growing Approach for crack-line detection from pavement images , 2011, Image Vis. Comput..

[12]  Anshuman Bhardwaj,et al.  UAVs as remote sensing platform in glaciology: Present applications and future prospects , 2016 .

[13]  Andrea Petracca,et al.  A real-time classification algorithm for EEG-based BCI driven by self-induced emotions , 2015, Comput. Methods Programs Biomed..

[14]  Paulo Lobato Correia,et al.  Automatic crack detection on road imagery using anisotropic diffusion and region linkage , 2010, 2010 18th European Signal Processing Conference.

[15]  Pierre Hornych,et al.  Use of Distributed Fiber Optic Sensors to Detect Damage in a Pavement , 2014 .

[16]  Yaping Wang,et al.  The Classification of Pavement Crack Image Based on Beamlet Algorithm , 2013, CCTA.

[17]  T. Saar,et al.  Automatic Asphalt pavement crack detection and classification using Neural Networks , 2010, 2010 12th Biennial Baltic Electronics Conference.

[18]  Soroush Mokhtari,et al.  Analytical study of computer vision-based pavement crack quantification using machine learning techniques , 2015 .

[19]  Cong Jin,et al.  A Hybrid Model Based on Mutual Information and Support Vector Machine for Automatic Image Annotation , 2015, CSOC.

[20]  K H McGhee,et al.  AUTOMATED PAVEMENT DISTRESS COLLECTION TECHNIQUES , 2004 .

[21]  Curt H. Davis,et al.  An integrated system for automatic road mapping from high-resolution multi-spectral satellite imagery by information fusion , 2005, Inf. Fusion.

[22]  Ferat Sahin,et al.  A survey on feature selection methods , 2014, Comput. Electr. Eng..

[23]  Mohamed S Kaseko,et al.  A neural network-based methodology for pavement crack detection and classification , 1993 .

[24]  Christian Koch,et al.  Automated Detection of Potholes in Visual Data , 2011, ISEC 2011.

[25]  Tatyana Kruglova,et al.  Robotic Laser Inspection of Airplane Wings Using Quadrotor , 2015 .

[26]  Sami F. Masri,et al.  A new methodology for non-contact accurate crack width measurement through photogrammetry for automated structural safety evaluation , 2013 .

[27]  Zhou Lili,et al.  An improved beamlet tree-structured algorithm and its application in pavement crack detection , 2012 .

[28]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[29]  Tim Duerden,et al.  An aura of confusion: 'seeing auras-vital energy or human physiology?' Part 1 of a three part series. , 2004, Complementary therapies in nursing & midwifery.

[30]  Hui-Huang Hsu,et al.  Hybrid feature selection by combining filters and wrappers , 2011, Expert Syst. Appl..

[31]  Stefan Glasauer,et al.  Do robots have goals? How agent cues influence action understanding in non-human primates , 2013, Behavioural Brain Research.

[32]  A. M. Khan,et al.  Image Segmentation Methods: A Comparative Study , 2013 .

[33]  S. M. Jong,et al.  High-resolution monitoring of Himalayan glacier dynamics using unmanned aerial vehicles , 2014 .

[34]  Chandra Kambhamettu,et al.  Performance Assessment of Flexible Pavements Using Active Contour Models , 2013 .

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

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

[37]  Nii O. Attoh-Okine,et al.  Functional Evaluation of Pavement Condition Using a Complete Vision System , 2014 .

[38]  Sami F. Masri,et al.  Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures , 2012 .

[39]  Carl T. Haas,et al.  A man-machine balanced rapid object model for automation of pavement crack sealing and maintenance , 2002 .

[40]  Yichang Tsai,et al.  Dynamic Programming and Connected Component Analysis for an Enhanced Pavement Distress Segmentation Algorithm , 2011 .

[41]  Bugao Xu,et al.  Automatic inspection of pavement cracking distress , 2005, SPIE Optics + Photonics.

[42]  Amri Napolitano,et al.  A comparative study of iterative and non-iterative feature selection techniques for software defect prediction , 2013, Information Systems Frontiers.

[43]  Shuguang Wu,et al.  A segment algorithm for crack dection , 2012, 2012 IEEE Symposium on Electrical & Electronics Engineering (EEESYM).

[44]  Vivek Kaul,et al.  Enhanced adaptive filter-bank-based automated pavement crack detection and segmentation system , 2012, J. Electronic Imaging.

[45]  Alberto Jardón,et al.  Past, present and future of robotic tunnel inspection , 2015 .

[46]  Jim Jing-Yan Wang,et al.  Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization , 2015, Expert Syst. Appl..

[47]  Wei Jianying.  Faulting estimation method based on vertical acceleration of cement concrete pavement , 2013 .

[48]  Li He,et al.  Automatic ridgelet image enhancement algorithm for road crack image based on fuzzy entropy and fuzzy divergence , 2009 .

[49]  Laurent Ciarletta,et al.  The AETOURNOS Project: Using a Flock of UAVs as a Cyber Physical System and Platform for Application-driven Research , 2012, ANT/MobiWIS.

[50]  Jose Miguel Puerta,et al.  A GRASP algorithm for fast hybrid (filter-wrapper) feature subset selection in high-dimensional datasets , 2011, Pattern Recognit. Lett..

[51]  Saeid Nahavandi,et al.  EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems , 2015, Expert Syst. Appl..

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

[53]  Y. Shen,et al.  A compressed sensing pavement distress image filtering algorithm based on NSCT domain , 2014 .

[54]  Eduardo Zalama Casanova,et al.  Road Crack Detection Using Visual Features Extracted by Gabor Filters , 2014, Comput. Aided Civ. Infrastructure Eng..

[55]  Ling Xu,et al.  Simple Procedure for Identifying Pavement Distresses from Video Images , 1994 .

[56]  Anthony Yezzi,et al.  Automating the Crack Map Detection Process for Machine Operated Crack Sealer , 2013 .

[57]  M. Esmel ElAlami.  A filter model for feature subset selection based on genetic algorithm , 2009, Knowl. Based Syst..

[58]  Anthony J. Yezzi,et al.  Detection of Curves with Unknown Endpoints using Minimal Path Techniques , 2010, BMVC.

[59]  Mohammad Hossein Moattar,et al.  Robust and stable feature selection by integrating ranking methods and wrapper technique in genetic data classification. , 2014, Biochemical and biophysical research communications.

[60]  Christoph Mertz,et al.  Vision for road inspection , 2014, IEEE Winter Conference on Applications of Computer Vision.

[61]  Higinio González-Jorge,et al.  Approach to identify cracking in asphalt pavement using GPR and infrared thermographic methods: Preliminary findings , 2014 .

[62]  Melanie Po-Leen Ooi,et al.  Low-Cost Microcontroller-based Hover Control Design of a Quadcopter , 2012 .

[63]  J. Wolpaw,et al.  P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls , 2015, Clinical Neurophysiology.

[64]  H. G. Gao,et al.  Pavement Crack Automatic Recognition Based on Wiener Filtering , 2009 .

[65]  Antonios Gasteratos,et al.  Semantic mapping for mobile robotics tasks: A survey , 2015, Robotics Auton. Syst..

[66]  Mohammad R. Jahanshahi,et al.  An innovative methodology for detection and quantification of cracks through incorporation of depth perception , 2011, Machine Vision and Applications.

[67]  Hangseok Choi,et al.  Effect of Biot’s coefficient and fluid properties on isothermal H-M coupled consolidation analysis of porous media , 2016 .

[68]  Tao Wang,et al.  Beamlet Transform Based Pavement Image Crack Detection , 2010, 2010 International Conference on Intelligent Computation Technology and Automation.

[69]  Wensheng Tang,et al.  Pavement Crack Segmentation Algorithm Based on Local Optimal Threshold of Cracks Density Distribution , 2011, ICIC.

[70]  Chunsun Zhang,et al.  An Unmanned Aerial Vehicle‐Based Imaging System for 3D Measurement of Unpaved Road Surface Distresses 1 , 2012, Comput. Aided Civ. Infrastructure Eng..

[71]  Fang Liu,et al.  Mutual-Information-Based Semi-Supervised Hyperspectral Band Selection With High Discrimination, High Information, and Low Redundancy , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[72]  Wenbin Ouyang,et al.  Pavement cracking measurements using 3D laser-scan images , 2013 .

[73]  Steven A. Velinsky,et al.  Operator controlled, vehicle-based highway crack-sealing machine , 2003 .

[74]  Jaching Chou,et al.  PAVEMENT DISTRESS EVALUATION USING FUZZY LOGIC AND MOMENT INVARIANTS , 1995 .

[75]  Paul W. Fieguth,et al.  A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure , 2015, Adv. Eng. Informatics.

[76]  Joarder Kamruzzaman,et al.  Search and tracking algorithms for swarms of robots: A survey , 2016, Robotics Auton. Syst..

[77]  LI Gang.  New Weighted Mean Filtering Algorithm for Surface Image Based on Grey Entropy , 2014 .

[78]  Jian Zhu,et al.  Illumination Invariant Enhancement and Threshold Segmentation Algorithm for Asphalt Pavement Crack Image , 2010, 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM).

[79]  Sylvie Chambon,et al.  Road Crack Extraction with Adapted Filtering and Markov Model-based Segmentation - Introduction and Validation , 2010, VISAPP.

[80]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[81]  Yi Yang,et al.  Semisupervised Feature Selection via Spline Regression for Video Semantic Recognition , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[82]  He Youquan,et al.  Studying of road crack image detection method based on the mathematical morphology , 2011, 2011 4th International Congress on Image and Signal Processing.

[83]  Paola Bandini,et al.  Prediction of Pavement Performance through Neuro‐Fuzzy Reasoning , 2010, Comput. Aided Civ. Infrastructure Eng..

[84]  David H Timm,et al.  A STUDY OF MANUAL VS AUTOMATED PAVEMENT CONDITION SURVEYS , 2004 .

[85]  Tong Heng Lee,et al.  Design and implementation of an autonomous flight control law for a UAV helicopter , 2009, Autom..

[86]  Paulo Lobato Correia,et al.  Improved road crack detection based on one-class Parzen density estimation and entropy reduction , 2010, 2010 IEEE International Conference on Image Processing.

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

[88]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[89]  Santosh Kumar Verma,et al.  Nanorobotics in dentistry – A review , 2014 .

[90]  Tim Duerden,et al.  An aura of confusion Part 2: the aided eye--"imaging the aura?". , 2004, Complementary therapies in nursing & midwifery.

[91]  Khurram Kamal,et al.  A Review of Three-Dimensional Imaging Technologies for Pavement Distress Detection and Measurements , 2015, IEEE Transactions on Intelligent Transportation Systems.

[92]  Fereidoon Moghadas Nejad,et al.  An expert system based on wavelet transform and radon neural network for pavement distress classification , 2011, Expert Syst. Appl..

[93]  James Wolfer,et al.  ASPHALT PAVEMENT CRACK CLASSIFICATION : A COMPARISON OF GA , MLP , AND SOM , 2005 .

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

[95]  Carla E. Brodley,et al.  Feature Selection for Unsupervised Learning , 2004, J. Mach. Learn. Res..

[96]  Amar R. Marathe,et al.  A regression method for estimating performance in a rapid serial visual presentation target detection task , 2016, Journal of Neuroscience Methods.

[97]  AhmedMahmoud,et al.  Toward low-cost 3D automatic pavement distress surveying: the close range photogrammetry approach , 2011 .

[98]  E. Salari,et al.  An image-based pavement distress detection and classification , 2012, 2012 IEEE International Conference on Electro/Information Technology.

[99]  Richard Weber,et al.  Kernel Penalized K-means: A feature selection method based on Kernel K-means , 2015, Inf. Sci..

[100]  Jianguo Jiang.  Crack Enhancement Algorithm Based on Improved EM , 2015 .

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

[102]  Pedro Arias,et al.  Low-cost aerial unit for outdoor inspection of building façades , 2013 .

[103]  Ezzatollah Salari,et al.  Pavement distress detection and severity analysis , 2011, Electronic Imaging.

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

[105]  Zhongyang Zheng,et al.  Research Advance in Swarm Robotics , 2013 .

[106]  J. M. Cadenas,et al.  Selecting Features from Low Quality Datasets by a Fuzzy Ensemble , 2012, IJCCI.

[107]  Jose A. Fernandez-Leon,et al.  How simple autonomous decisions evolve into robust behaviours?: A review from neurorobotics, cognitive, self-organized and artificial immune systems fields , 2014, Biosyst..

[108]  Honglun Wang,et al.  UAV feasible path planning based on disturbed fluid and trajectory propagation , 2015 .

[109]  Heng-Da Cheng,et al.  Novel fuzzy logic approach to pavement distress detection , 1996, Smart Structures.

[110]  Shih-Chung Kang,et al.  A lightweight bridge inspection system using a dual-cable suspension mechanism , 2014 .

[111]  Qi Mao,et al.  Feature selection for unsupervised learning through local learning , 2015, Pattern Recognit. Lett..

[112]  John Atkinson,et al.  Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers , 2016, Expert Syst. Appl..

[113]  P. Zarco-Tejada,et al.  Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera , 2012 .

[114]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[115]  Paulo Lobato Correia,et al.  Accelerated unsupervised filtering for the smoothing of road pavement surface imagery , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

[116]  P. Sajda,et al.  Cortically coupled computer vision for rapid image search , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[117]  Norman Wittels,et al.  ANALYSIS AND GENERATION OF PAVEMENT DISTRESS IMAGES USING FRACTALS , 1991 .

[118]  Deng Cai,et al.  Unsupervised feature selection for multi-cluster data , 2010, KDD.

[119]  Sten Sundin,et al.  ARTIFICIAL INTELLIGENCE--BASED DECISION SUPPORT TECHNOLOGIES IN PAVEMENT MANAGEMENT , 2001 .

[120]  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).

[121]  Hanyu Hong,et al.  Pavement crack detection based on texture feature , 2011, International Symposium on Multispectral Image Processing and Pattern Recognition.

[122]  Sylvie Chambon,et al.  Automatic Road Defect Detection by Textural Pattern Recognition Based on AdaBoost , 2012, Comput. Aided Civ. Infrastructure Eng..

[123]  Yaping Wang,et al.  Edge Detection In Pavement Crack Image With Beamlet Transform , 2012, EMEIT 2012.

[124]  Gang Li,et al.  Fuzzy contrast enhancement algorithm for road surface image based on adaptively changing index via grey entropy , 2013 .

[125]  Ralph Haas,et al.  Pavement Asset Management , 2015 .

[126]  Khurram Kamal,et al.  Metrology and visualization of potholes using the microsoft kinect sensor , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[127]  Gamini Dissanayake,et al.  Human–robot–environment interaction interface for robotic grit-blasting of complex steel bridges , 2012 .

[128]  B Basavaprasad.  A COMPARATIVE STUDY ON CLASSIFICATION OF IMAGE SEGMENTATION METHODS WITH A FOCUS ON GRAPH BASED TECHNIQUES , 2014 .

[129]  Carl T. Haas,et al.  Path planning for machine vision assisted, teleoperated pavement crack sealer , 1998 .

[130]  Dae-Won Kim,et al.  Mutual Information-based multi-label feature selection using interaction information , 2015, Expert Syst. Appl..

[131]  Fereidoon Moghadas Nejad,et al.  An optimum feature extraction method based on Wavelet-Radon Transform and Dynamic Neural Network for pavement distress classification , 2011, Expert Syst. Appl..

[132]  Aimin Sha,et al.  Pavement crack classification based on chain code , 2010, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery.

[133]  Ioannis Brilakis,et al.  Patch detection for pavement assessment , 2015 .

[134]  Jia-Ruey Chang,et al.  Strategies for autonomous robots to inspect pavement distresses , 2011 .

[135]  Wai Yeung Yan,et al.  Urban land cover classification using airborne LiDAR data: A review , 2015 .

[136]  Akira Kawamura,et al.  Automatic detection method of localized pavement roughness using quarter car model by lifting wavelet filters , 2013 .

[137]  Fereidoon Moghadas Nejad,et al.  A comparison of multi-resolution methods for detection and isolation of pavement distress , 2011, Expert Syst. Appl..

[138]  Qiuqi Ruan,et al.  Hessian Semi-Supervised Sparse Feature Selection Based on ${L_{2,1/2}}$ -Matrix Norm , 2015, IEEE Transactions on Multimedia.

[139]  Mohamed S Kaseko,et al.  COMPARISON OF TRADITIONAL AND NEURAL CLASSIFIERS FOR PAVEMENT-CRACK DETECTION. , 1994 .

[140]  Wen Chang-ping.  Bayes discriminant analysis method of rock-mass quality classification , 2008 .

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

[142]  Emanuele Frontoni,et al.  Road pavement crack automatic detection by MMS images , 2013, 21st Mediterranean Conference on Control and Automation.

[143]  Qingquan Li,et al.  CrackTree: Automatic crack detection from pavement images , 2012, Pattern Recognit. Lett..

[144]  Patrick J Grandsaert.  Integrating Pavement Crack Detection and Analysis Using Autonomous Unmanned Aerial Vehicle Imagery , 2015 .

[145]  Bugao Xu,et al.  Fusing complementary images for pavement cracking measurements , 2015 .

[146]  Yong Wang,et al.  ISAR Imaging of Rotating Target with Equal Changing Acceleration Based on the Cubic Phase Function , 2008, EURASIP J. Adv. Signal Process..

[147]  Xiaoming Sun,et al.  Pavement crack characteristic detection based on sparse representation , 2012, EURASIP J. Adv. Signal Process..

[148]  Xiaoming Xu,et al.  A hybrid genetic algorithm for feature selection wrapper based on mutual information , 2007, Pattern Recognit. Lett..

[149]  Richard W. Miller,et al.  Assessment of Image-Based Data Collection and the AASHTO Provisional Standard for Cracking on Asphalt-Surfaced Pavements , 2004 .

[150]  Huidrom Lokeshwor,et al.  Robust Method for Automated Segmentation of Frames with/without Distress from Road Surface Video Clips , 2014 .

[151]  Glenn Platt,et al.  Unsupervised feature selection using swarm intelligence and consensus clustering for automatic fault detection and diagnosis in Heating Ventilation and Air Conditioning systems , 2015, Appl. Soft Comput..

[152]  Yamin Zhang,et al.  Research on pavement crack recognition methods based on image processing , 2011, International Conference on Digital Image Processing.

[153]  Xiaoming Xu,et al.  A parameterless feature ranking algorithm based on MI , 2008, Neurocomputing.

[154]  Guo Yanqing,et al.  Algorithm on Contourlet Domain in Detection of Road Cracks for Pavement Images , 2013 .

[155]  Habibollah Haron,et al.  Semi-supervised SVM-based Feature Selection for Cancer Classification using Microarray Gene Expression Data , 2015, IEA/AIE.

[156]  Gaurav S. Sukhatme,et al.  A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures , 2009 .

[157]  Russell M. Mersereau,et al.  Critical Assessment of Pavement Distress Segmentation Methods , 2010 .

[158]  Sami F. Masri,et al.  A Novel Crack Detection Approach for Condition Assessment of Structures , 2011 .

[159]  Alistair A. Young,et al.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 2017, MICCAI 2017.

[160]  Nii O. Attoh-Okine,et al.  The Empirical Mode Decomposition and the Hilbert-Huang Transform , 2008, EURASIP J. Adv. Signal Process..

[161]  C. A. Murthy,et al.  Unsupervised Feature Selection Using Feature Similarity , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[162]  Yanliang Gu,et al.  Automatic Crack Detection and Segmentation Using a Hybrid Algorithm for Road Distress Analysis , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[163]  Levent Bayındır,et al.  A review of swarm robotics tasks , 2016, Neurocomputing.

[164]  Edward K. Wong,et al.  PAVEMENT DISTRESS ANALYSIS USING IMAGE PROCESSING TECHNIQUES , 1991 .

[165]  Allen B. Downey,et al.  ANALYSIS OF SEGMENTATION ALGORITHMS FOR PAVEMENT DISTRESS IMAGES , 1993 .

[166]  Roberto Battiti,et al.  Using mutual information for selecting features in supervised neural net learning , 1994, IEEE Trans. Neural Networks.

[167]  Mark A. Hall,et al.  Correlation-based Feature Selection for Machine Learning , 2003 .

[168]  Li Li,et al.  An Efficient Way in Image Preprocessing for Pavement Crack Images , 2012 .

[169]  Jeong Ho Lee,et al.  Bridge inspection robot system with machine vision , 2009 .

[170]  Peisen S. Huang,et al.  Wavelet-Based Pavement Distress Classification , 2005 .

[171]  Xinyu Yang,et al.  Automation Recognition of Pavement Surface Distress Based on Support Vector Machine , 2009, 2009 Second International Conference on Intelligent Networks and Intelligent Systems.

[172]  John Hubacher.  The phantom leaf effect: a replication, part 1. , 2015, Journal of alternative and complementary medicine.

[173]  Hamad Karki,et al.  Application of robotics in onshore oil and gas industry - A review Part I , 2016, Robotics Auton. Syst..

[174]  Stephanie W. Graves.  Electro-optical sensor evaluation of airfield pavement , 2013 .

[175]  Ezzatollah Salari,et al.  Beamlet Transform‐Based Technique for Pavement Crack Detection and Classification , 2010, Comput. Aided Civ. Infrastructure Eng..

[176]  M Jerry H Mohajeri,et al.  ARIA (TRADEMARK): AN OPERATING SYSTEM OF PAVEMENT DISTRESS DIAGNOSIS BY IMAGE PROCESSING , 1991 .

[177]  Tarek Hamel,et al.  A UAV for bridge inspection: Visual servoing control law with orientation limits , 2007 .

[178]  Hong Hu,et al.  Feature selection using the hybrid of ant colony optimization and mutual information for the forecaster , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[179]  Wang Tao,et al.  Proximal Support Vector Machine Based Pavement Image Classification , 2011 .

[180]  Byoung Jik Lee,et al.  Position‐Invariant Neural Network for Digital Pavement Crack Analysis , 2004 .

[181]  Paulo Lobato Correia,et al.  Supervised Crack Detection and Classification in Images of Road Pavement Flexible Surfaces , 2009 .

[182]  Jennifer G. Dy.  Unsupervised Feature Selection , 2007 .

[183]  Lou Jing,et al.  Pavement Crack Distress Detection Based on Image Analysis , 2010, 2010 International Conference on Machine Vision and Human-machine Interface.

[184]  Xinxin Niu,et al.  Automatic Recognition of Pavement Surface Crack Based on BP Neural Network , 2008, 2008 International Conference on Computer and Electrical Engineering.

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

[186]  Gang Li,et al.  Improved Pavement Distress Detection Based on Contourlet Transform and Multi-Direction Morphological Structuring Elements , 2012 .

[187]  Thegaran Naidoo,et al.  Visual surveying platform for the automated detection of road surface distresses , 2014, Other Conferences.

[188]  Hui Zhang,et al.  Image segmentation evaluation: A survey of unsupervised methods , 2008, Comput. Vis. Image Underst..

[189]  Heidar Ali Talebi,et al.  UAV-UGVs cooperation: With a moving center based trajectory , 2015, Robotics Auton. Syst..

[190]  Ioannis Brilakis,et al.  Detection of large-scale concrete columns for automated bridge inspection , 2010 .

[191]  Chun-Xia Zhao,et al.  Pavement Distress Detection Based on Nonsubsampled Contourlet Transform , 2008, 2008 International Conference on Computer Science and Software Engineering.

[192]  Anis Koubaa,et al.  Five Traits of Performance Enhancement Using Cloud Robotics: A Survey , 2014, EUSPN/ICTH.

[193]  Paulo Lobato Correia,et al.  CrackIT — An image processing toolbox for crack detection and characterization , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[194]  Ning Zhang,et al.  A novel road crack detection and identification method using digital image processing techniques , 2012, 2012 7th International Conference on Computing and Convergence Technology (ICCCT).

[195]  Witold Pedrycz,et al.  Unsupervised feature selection via maximum projection and minimum redundancy , 2015, Knowl. Based Syst..

[196]  Myo Taeg Lim,et al.  Robot-based construction automation: An application to steel beam assembly (Part II) , 2013 .

[197]  Wenbo Luo,et al.  Attentional biases among body-dissatisfied young women: an ERP study with rapid serial visual presentation. , 2011, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[198]  Ezzatollah Salari,et al.  Pavement distress detection and classification using a Genetic Algorithm , 2011, 2011 IEEE Applied Imagery Pattern Recognition Workshop (AIPR).

[199]  Guoqiang Wang,et al.  The Segmentation Algorithm for Pavement Cracking Images Based on the Improved Fuzzy Clustering , 2013 .

[200]  Ghada S. Moussa,et al.  A New Technique for Automatic Detection and Parameters Estimation of Pavement Crack , 2011 .

[201]  Feng Li,et al.  Critical Assessment of Detecting Asphalt Pavement Cracks under Different Lighting and Low Intensity Contrast Conditions Using Emerging 3D Laser Technology , 2012 .

[202]  Wenfeng Feng,et al.  Three stages of facial expression processing: ERP study with rapid serial visual presentation , 2010, NeuroImage.

[203]  Nidhi Chandrakar,et al.  Study and comparison of various image edge detection techniques , 2012 .

[204]  JaChing Chou,et al.  Pavement distress classification using neural networks , 1994, Proceedings of IEEE International Conference on Systems, Man and Cybernetics.

[205]  Paulo Lobato Correia,et al.  Supervised strategies for cracks detection in images of road pavement flexible surfaces , 2008, 2008 16th European Signal Processing Conference.

[206]  Xiaoming Sun,et al.  A Pavement Crack Detection Method Combining 2D with 3D Information Based on Dempster‐Shafer Theory , 2014, Comput. Aided Civ. Infrastructure Eng..

[207]  C. Mala,et al.  A Survey on Monochrome Image Segmentation Methods , 2012 .

[208]  Kelvin C. P. Wang,et al.  Wavelet-Based Pavement Distress Image Edge Detection with À Trous Algorithm , 2007 .

[209]  Nigel Waters,et al.  Review of remote sensing methodologies for pavement management and assessment , 2015 .

[210]  Heng-Da Cheng.  Automated real-time pavement distress detection using fuzzy logic and neural network , 1996, Smart Structures.

[211]  Anthony J. Yezzi,et al.  Detecting Curves with Unknown Endpoints and Arbitrary Topology Using Minimal Paths , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[212]  Wang Xiaohu,et al.  Image Edge Detection Based on Beamlet Transform , 2012 .

[213]  Ramaswamy Palaniappan,et al.  Multiresolution analysis over graphs for a motor imagery based online BCI game , 2016, Comput. Biol. Medicine.

[214]  Huang We.  Preliminary Study of Pavement Surface Distress Automation Recognition Based on Wavelet Neural Network , 2004 .

[215]  Xin Feng,et al.  Pavement distress detection and classification with automated image processing , 2011, Proceedings 2011 International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE).

[216]  Jitender Kumar Chhabra,et al.  Automatic Unsupervised Feature Selection using Gravitational Search Algorithm , 2015 .

[217]  Ivan Lee,et al.  Mutual information for enhanced feature selection in visual tracking , 2015, Defense + Security Symposium.

[218]  Mahmood Fathy,et al.  A classified and comparative study of edge detection algorithms , 2002, Proceedings. International Conference on Information Technology: Coding and Computing.

[219]  Richard J. Duro,et al.  Autonomous UAV based search operations using Constrained Sampling Evolutionary Algorithms , 2014, Neurocomputing.

[220]  Chengdong Wu,et al.  Pavement image denoising based on shearlet treansform , 2011, Proceedings of 2011 International Conference on Electronics and Optoelectronics.

[221]  Yao Li,et al.  Experimental Study on the Compressibility of Cement Improved Laterite Soil , 2012 .

[222]  Yozo Fujino,et al.  Concrete Crack Detection by Multiple Sequential Image Filtering , 2012, Comput. Aided Civ. Infrastructure Eng..

[223]  Zhou Huilin,et al.  Evolving Fuzzy Neural Network for Highway Subsurface Condition Evaluation using Ground Penetrating Radar , 2011 .

[224]  Yichang Tsai,et al.  Multiscale Crack Fundamental Element Model for Real-World Pavement Crack Classification , 2014, J. Comput. Civ. Eng..

[225]  Chunxia Zhao,et al.  Pavement Cracks Detection Based on FDWT , 2009, 2009 International Conference on Computational Intelligence and Software Engineering.

[226]  Eckart Michaelsen,et al.  Stochastic reasoning for structural pattern recognition: An example from image-based UAV navigation , 2014, Pattern Recognit..

[227]  Ezzatollah Salari,et al.  Beamlet transform based technique for pavement image processing and classification , 2009, 2009 IEEE International Conference on Electro/Information Technology.

[228]  Burcin Becerik-Gerber,et al.  Unsupervised Approach for Autonomous Pavement-Defect Detection and Quantification Using an Inexpensive Depth Sensor , 2013, J. Comput. Civ. Eng..

[229]  Halil Ceylan,et al.  Neural Networks Applications in Pavement Engineering: A Recent Survey , 2014 .

[230]  Jérôme Idier,et al.  A new minimal path selection algorithm for automatic crack detection on pavement images , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[231]  Jochen Teizer,et al.  Mobile 3D mapping for surveying earthwork projects using an Unmanned Aerial Vehicle (UAV) system , 2014 .

[232]  Özge Uncu,et al.  A novel feature selection approach: Combining feature wrappers and filters , 2007, Inf. Sci..

[233]  Kun Xu,et al.  Pavement crack image detection algorithm under nonuniform illuminance , 2013, 2013 IEEE Third International Conference on Information Science and Technology (ICIST).

[234]  Cheng Wang,et al.  Automated Road Information Extraction From Mobile Laser Scanning Data , 2015, IEEE Transactions on Intelligent Transportation Systems.

[235]  N. Senthilkumaran,et al.  Image Segmentation - A Survey of Soft Computing Approaches , 2009, 2009 International Conference on Advances in Recent Technologies in Communication and Computing.

[236]  Alfred Stein,et al.  Region-based urban road extraction from VHR satellite images using Binary Partition Tree , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[237]  Heng-Da Cheng,et al.  Novel Approach to Pavement Cracking Detection Based on Neural Network , 2001 .

[238]  Jin Lin,et al.  Potholes Detection Based on SVM in the Pavement Distress Image , 2010, 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science.

[239]  Carlos Balaguer,et al.  Robot-aided tunnel inspection and maintenance system by vision and proximity sensor integration , 2011 .

[240]  Christoph Mertz,et al.  1 CITY-WIDE ROAD DISTRESS MONITORING WITH SMARTPHONES , 2014 .

[241]  Robert X. Gao,et al.  Pavement Distress Analysis based on Dual-Tree Complex Wavelet Transform , 2012 .

[242]  J. Ramiro Martinez de Dios,et al.  Testbeds for ubiquitous robotics: A survey , 2013, Robotics Auton. Syst..

[243]  E. Salari,et al.  Pavement distress detection and classification using feature mapping , 2010, 2010 IEEE International Conference on Electro/Information Technology.

[244]  Yixian Yang,et al.  Novel Approach to Pavement Cracking Automatic Detection Based on Segment Extending , 2008, 2008 International Symposium on Knowledge Acquisition and Modeling.

[245]  Han Tong,et al.  Path Planning of UAV Based on Voronoi Diagram and DPSO , 2012 .

[246]  Rahul Rai,et al.  Fragmentary shape recognition: A BCI study , 2016, Comput. Aided Des..

[247]  Marc Moonen,et al.  Joint DOA and multi-pitch estimation based on subspace techniques , 2012, EURASIP J. Adv. Signal Process..

[248]  Jidong Zhao,et al.  Locality sensitive semi-supervised feature selection , 2008, Neurocomputing.

[249]  F. Blais,et al.  Automated pavement distress data collection and analysis: a 3-D approach , 1997, Proceedings. International Conference on Recent Advances in 3-D Digital Imaging and Modeling (Cat. No.97TB100134).

[250]  Peihe Tang,et al.  A new method to pavement cracking detection based on the Biological Inspired Model , 2012, 2012 International Conference on Computer Science and Information Processing (CSIP).

[251]  R. Maini.  Study and Comparison of Various Image Edge Detection Techniques , 2004 .

[252]  Y. Ahmet Sekercioglu,et al.  A special issue of Ad Hoc Networks on "Theory, algorithms and applications of wireless networked robotics" , 2013, Ad Hoc Networks.

[253]  Haroun Rababaah.  Asphalt Pavement Crack Classification : A Comparative Study of Three AI Approaches: Multilayer Perceptron, Genetic Algorithms and Self-Organizing Maps , 2005 .

[254]  Farhad Samadzadegan,et al.  A new ant based distributed framework for urban road map updating from high resolution satellite imagery , 2013, Comput. Geosci..

[255]  Mathias Weymar,et al.  New learning following reactivation in the human brain: Targeting emotional memories through rapid serial visual presentation , 2015, Neurobiology of Learning and Memory.

[256]  Qiang Li,et al.  Matched Filtering Algorithm for Pavement Cracking Detection , 2013 .

[257]  Xue Li,et al.  A Neural Network based Technique for Automatic Classification of Road Cracks , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[258]  Minho Kim,et al.  Quadcopter flight control using a low-cost hybrid interface with EEG-based classification and eye tracking , 2014, Comput. Biol. Medicine.

[259]  He Li.  Image Enhancement Algorithm on Ridgelet Domain in Detection of Road Cracks , 2009 .

[260]  Huan Liu,et al.  Semi-supervised Feature Selection via Spectral Analysis , 2007, SDM.

[261]  Mani Golparvar-Fard,et al.  Segmentation and Recognition of Highway Assets Using Image-Based 3D Point Clouds and Semantic Texton Forests , 2015, J. Comput. Civ. Eng..

[262]  Serdal Terzi,et al.  Modeling for pavement roughness using the ANFIS approach , 2013, Adv. Eng. Softw..

[263]  Hyun-Seok Yoo,et al.  Development of a crack recognition algorithm from non-routed pavement images using artificial neural network and binary logistic regression , 2016 .

[264]  P. Correia,et al.  Automatic Road Pavement Crack Detection using SVM , 2012 .

[265]  Russell M. Mersereau,et al.  Quantitative Performance Evaluation Algorithms for Pavement Distress Segmentation , 2010 .

[266]  José Rouillard,et al.  Hybrid BCI Coupling EEG and EMG for Severe Motor Disabilities , 2015 .

[267]  Khurram Kamal,et al.  Pavement crack detection using the Gabor filter , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[268]  Nils Bertschinger,et al.  Adaptive Information-Theoretical Feature Selection for Pattern Classification , 2012, IJCCI.

[269]  Silvia Rossi,et al.  An adaptive oscillatory neural architecture for controlling behavior based robotic systems , 2010, Neurocomputing.

[270]  Dennis J. McFarland,et al.  P 300-based brain-computer interface ( BCI ) event-related potentials ( ERPs ) : People with amyotrophic lateral sclerosis ( ALS ) vs . age-matched controls , 2015 .

[271]  Peter Kovesi,et al.  Semi-automatic mapping of geological Structures using UAV-based photogrammetric data: An image analysis approach , 2014, Comput. Geosci..

[272]  Lei Liu,et al.  Feature selection with dynamic mutual information , 2009, Pattern Recognit..

[273]  Zongren Wang.  Formulation and Assessment of a Customizable Procedure for Pavement Distress Index , 2000 .

[274]  Hongwei Hao,et al.  Selecting feature subset with sparsity and low redundancy for unsupervised learning , 2015, Knowl. Based Syst..

[275]  Yichang Tsai,et al.  An Automated Filter Bank-Based Pavement Crack Detection System Incorporating Standard Compression Coders , 2012 .

[276]  Mohammad Hossein Fazel Zarandi,et al.  A multi-stage expert system for classification of pavement cracking , 2013, 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS).

[277]  Carl T. Haas,et al.  Man-Machine Balanced Crack Sealing Process for UT Automated Road Maintenance Machine , 1998 .

[278]  Jukka Riekki,et al.  Applying Wavelet Packet Decomposition and One-Class Support Vector Machine on Vehicle Acceleration Traces for Road Anomaly Detection , 2013, ISNN.

[279]  Fereidoon Moghadas Nejad,et al.  The Hybrid Method and its Application to Smart Pavement Management , 2013 .

[280]  Sue McNeil,et al.  EVALUATION OF ERRORS IN AUTOMATED PAVEMENT-DISTRESS DATA ACQUISITION , 1991 .

[281]  Heng-Da Cheng,et al.  Novel Approach to Pavement Cracking Detection Based on Fuzzy Set Theory , 1999 .

[282]  H. Zakeri,et al.  A new automatic MF generator (AMFG) for general 3D type-ii fuzzy in the polar frame , 2014, 2014 IEEE Conference on Norbert Wiener in the 21st Century (21CW).

[283]  Imad L. Al-Qadi,et al.  Airfield and Highway Pavement 2013: Sustainable and Efficient Pavements , 2013 .

[284]  Gregory R. Madey,et al.  Control of Artificial Swarms with DDDAS , 2014, ICCS.

[285]  Hamad Karki,et al.  Application of robotics in offshore oil and gas industry - A review Part II , 2016, Robotics Auton. Syst..

[286]  Hanyun Wang,et al.  Iterative Tensor Voting for Pavement Crack Extraction Using Mobile Laser Scanning Data , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[287]  A Wright,et al.  Automatic identification of cracks in road surfaces , 1999 .

[288]  Beibei Song.  Statistics Properties of Asphalt Pavement Images for Cracks Detection , 2013 .

[289]  Allen B. Downey,et al.  Primitive-Based Classification of Pavement Cracking Images , 1993 .

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

[291]  Jia-Ruey Chang,et al.  Dual-Light Inspection Method for Automatic Pavement Surveys , 2013, J. Comput. Civ. Eng..

[292]  Wang Weixing,et al.  Pavement Crack Detection by Ridge Detection on Fractional Calculus and Dual-thresholds , 2015, MUE 2015.