Image based techniques for crack detection, classification and quantification in asphalt pavement: a review. (English) Zbl 1393.94128
Summary: 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.
MSC:
| 94A08 | Image processing (compression, reconstruction, etc.) in information and communication theory |
| 94A13 | Detection theory in information and communication theory |
| 62H35 | Image analysis in multivariate analysis |
| 74Rxx | Fracture and damage |
| 94-02 | Research exposition (monographs, survey articles) pertaining to information and communication theory |
References:
| [1] | Abdel-Qader, I; Abudayyeh, O; Kelly, ME, Analysis of edge-detection techniques for crack identification in bridges, J Comput Civ Eng, 17, 255-263, (2003) |
| [2] | Adu-Gyamfi, Y; Okine, NA; Garateguy, G; Carrillo, R; Arce, GR, Multiresolution information mining for pavement crack image analysis, J Comput Civ Eng, 26, 741-749, (2011) |
| [3] | Adu-Gyamfi YO, Kambhamettu C, Okine NA (2013) Performance assessment of flexible pavements using active contour models. In: 2013 Airfield and highway pavement conference: sustainable and efficient pavements. Los Angeles |
| [4] | Adu-Gyamfi, YO; Tienaah, T; Attoh-Okine, NO; Kambhamettu, C, Functional evaluation of pavement condition using a complete vision system, J Transp Eng, 140, 04014040, (2014) |
| [5] | Aghaeeyan, A; Abdollahi, F; Talebi, HA, UAV-UGVs cooperation: with a moving center based trajectory, Robot Auton Syst, 63, 1-9, (2015) |
| [6] | Ahmed, M; Haas, CT; Haas, R, Toward low-cost 3D automatic pavement distress surveying: the close range photogrammetry approach, Can J Civ Eng, 38, 1301-1313, (2011) |
| [7] | Amhaz R, Chambon S, Idier J, Baltazart V (2014) A new minimal path selection algorithm for automatic crack detection on pavement images. In: 2014 IEEE international conference on image processing, ICIP 2014. Institute of Electrical and Electronics Engineers Inc |
| [8] | Ang JC, Haron H, Hamed HNA (2015) Semi-supervised SVM-based feature selection for cancer classification using microarray gene expression data. Current approaches in applied artificial intelligence. Springer, New York, pp 468-477 |
| [9] | Asensio-Cubero, J; Gan, JQ; Palaniappan, R, Multiresolution analysis over graphs for a motor imagery based online BCI game, Comput Biol Med, 68, 21-26, (2016) |
| [10] | Atkinson, J; Campos, D, Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers, Exp Syst Appl, 47, 35-41, (2016) |
| [11] | Attoh-Okine, N; Barner, K; Bentil, D; Zhang, R, The empirical mode decomposition and the Hilbert-huang transform, EURASIP J Adv Signal Process, 2008, 1-2, (2008) · Zbl 1188.94004 |
| [12] | Avdiyenko L, Bertschinger N, Jost J (2015) Adaptive information-theoretical feature selection for pattern classification. Computational intelligence. Springer, New York, pp 279-294 · Zbl 1348.68209 |
| [13] | Avila M, Begot S, Duculty F, Nguyen TS (2014) 2D image based road pavement crack detection by calculating minimal paths and dynamic programming. In: 2014 IEEE international conference on image processing, ICIP 2014 |
| [14] | Ayenu-Prah, A; Attoh-Okine, N, Evaluating pavement cracks with bidimensional empirical mode decomposition, EURASIP J Adv Signal Process, 2008, 861701, (2008) · Zbl 1184.94013 |
| [15] | Basavaprasad, B; Ravi, M, A comparative study on classification of image segmentation methods with a focus on graph based techniques, Int J Res Eng Technol, 3, 310-314, (2014) |
| [16] | Battiti, R, Using mutual information for selecting features in supervised neural net learning, IEEE Trans Neural Netw, 5, 537-550, (1994) |
| [17] | Bayındır, L, A review of swarm robotics tasks, Neurocomputing, 172, 292-321, (2016) |
| [18] | Bermejo, P; Gámez, JA; Puerta, JM, A GRASP algorithm for fast hybrid (filter-wrapper) feature subset selection in high-dimensional datasets, Pattern Recognit Lett, 32, 701-711, (2011) |
| [19] | Bhardwaj, A; Sam, L; Akanksha, F; Martín-Torres, J; Kumar, R, UAVs as remote sensing platform in glaciology: present applications and future prospects, Remote Sens Environ, 175, 196-204, (2016) |
| [20] | Bianchini, A; Bandini, P, Prediction of pavement performance through neuro-fuzzy reasoning, Comput Aided Civ Infrastruct Eng, 25, 39-54, (2010) |
| [21] | Bray J, Verma B, Li X, He W (2006) A neural network based technique for automatic classification of road cracks. In: International joint conference on neural networks, 2006. IJCNN’06. IEEE |
| [22] | Burattini, E; Gregorio, M; Rossi, S, An adaptive oscillatory neural architecture for controlling behavior based robotic systems, Neurocomputing, 73, 2829-2836, (2010) |
| [23] | Bursanescu L, Blais F (1997) Automated pavement distress data collection and analysis: a 3-D approach |
| [24] | Cadenas J, Garrido M, Martínez R (2015) Selecting features from low quality datasets by a fuzzy ensemble. Computational intelligence. Springer, New York, pp 229-243 |
| [25] | Cai D, Zhang C, He X (2010) Unsupervised feature selection for multi-cluster data. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining. ACM |
| [26] | Cai Y, Zhang Y (2011) Research on pavement crack recognition methods based on image processing. In: 3rd International conference on digital image processing, ICDIP 2011. Chengdu |
| [27] | Canny, J, A computational approach to edge detection, IEEE Trans Pattern Anal Mach Intell, 6, 679-698, (1986) |
| [28] | Ceylan, H; Bayrak, MB; Gopalakrishnan, K, Neural networks applications in pavement engineering: a recent survey, Int J Pavement Res Technol, 7, 434-444, (2014) |
| [29] | Chambon S, Gourraud C, Moliard JM, Nicolle P (2010) Road crack extraction with adapted filtering and Markov model-based segmentation: introduction and validation. In: 5th International conference on computer vision theory and applications, VISAPP 2010. Angers |
| [30] | Chambon, S; Moliard, J, Automatic road pavement assessment with image processing: review and comparison, Int J Geophys, 2011, 20, (2011) |
| [31] | Chandrashekar, G; Sahin, F, A survey on feature selection methods, Comput Electr Eng, 40, 16-28, (2014) |
| [32] | Chang-ping, W, Bayes discriminant analysis method of rock-mass quality classification, J China Coal Soc, 33, 395-399, (2008) |
| [33] | Changxia M, Wenming W, Chunxia Z, Feng D, Zhengli Z (2009) Pavement cracks detection based on FDWT. In: International conference on computational intelligence and software engineering, 2009. CiSE 2009 |
| [34] | Chapeleau X, Blanc J, Hornych P, Gautier JL, Carroget J (2014) Use of distributed fiber optic sensors to detect damage in a pavement. In: Asphalt pavements: proceedings of the international conference on asphalt pavements, ISAP 2014 |
| [35] | Chen, Y-C; Yang, C-E; Kang, S-C, A lightweight bridge inspection system using a dual-cable suspension mechanism, Autom Constr, 46, 52-63, (2014) |
| [36] | Cheng H-D (1996) Automated real-time pavement distress detection using fuzzy logic and neural network. In: Nondestructive evaluation techniques for aging infrastructure and manufacturing. International Society for Optics and Photonics |
| [37] | Cheng H-D, Chen J-R, Glazier C (1996) Novel fuzzy logic approach to pavement distress detection. In: Nondestructive evaluation techniques for aging infrastructure and manufacturing. International Society for Optics and Photonics |
| [38] | Cheng, H; Chen, J-R; Glazier, C; Hu, Y, Novel approach to pavement cracking detection based on fuzzy set theory, J Comput Civ Eng, 13, 270-280, (1999) |
| [39] | Cheng, H; Wang, J; Hu, Y; Glazier, C; Shi, X; Chen, X, Novel approach to pavement cracking detection based on neural network, Trans Res Rec J Transp Res Board, 1764, 119-127, (2001) |
| [40] | Chotiprayanakul, P; Liu, DK; Dissanayake, G, Human-robot-environment interaction interface for robotic grit-blasting of complex steel bridges, Autom Constr, 27, 11-23, (2012) |
| [41] | Chou J, O’Neill WA, Cheng H (1994) Pavement distress classification using neural networks. In: IEEE international conference on systems, man, and cybernetics, 1994. Humans, information and technology, 1994. IEEE |
| [42] | Chou, J; O’Neill, WA; Cheng, H, Pavement distress evaluation using fuzzy logic and moment invariants, Transp Res Rec, 1505, 39-46, (1995) |
| [43] | Chua, KM; Xu, L, Simple procedure for identifying pavement distresses from video images, J Transp Eng, 120, 412-431, (1994) |
| [44] | Chu, B; Jung, K; Lim, M-T; Hong, D, Robot-based construction automation: an application to steel beam assembly (part I), Autom Constr, 32, 46-61, (2013) |
| [45] | Chu, J-W; Chu, X-M; Wang, R-B; Shi, S-M, Research on asphalt pavement surface distress image feature extraction method, J Image Graph, 10, 1211-1217, (2003) |
| [46] | Colomina, I; Molina, P, Unmanned aerial systems for photogrammetry and remote sensing: a review, ISPRS J Photogramm Remote Sens, 92, 79-97, (2014) |
| [47] | Cong F, Hautakangas H, Nieminen J, Mazhelis O, Perttunen M, Riekki J, Ristaniemi T (2013) Applying wavelet packet decomposition and one-class support vector machine on vehicle acceleration traces for road anomaly detection. Advances in neural networks-ISNN 2013. Springer, New York, pp 291-299 |
| [48] | Cord, A; Chambon, S, Automatic road defect detection by textural pattern recognition based on adaboost, Comput Aided Civ Infrastruct Eng, 27, 244-259, (2012) |
| [49] | Doquire, G; Verleysen, M, Mutual information-based feature selection for multilabel classification, Neurocomputing, 122, 148-155, (2013) · Zbl 1297.68202 |
| [50] | Duerden, T, An aura of confusion part 2: the aided eye—‘imaging the aura?’, ’Complement Ther Nurs Midwifery, 10, 116-123, (2004) |
| [51] | Duerden, T, An aura of confusion: ‘seeing auras—vital energy or human physiology?’part 1 of a three part series, Complement Ther Nurs Midwifery, 10, 22-29, (2004) |
| [52] | Dy JG (2008) Unsupervised feature selection. Comput Methods Feature Sel 19-39 |
| [53] | Dy, JG; Brodley, CE, Feature selection for unsupervised learning, J Mach Learn Res, 5, 845-889, (2004) · Zbl 1222.68187 |
| [54] | ElAlami, ME, A filter model for feature subset selection based on genetic algorithm, Knowl Based Syst, 22, 356-362, (2009) |
| [55] | Fawcett, T, An introduction to ROC analysis, Pattern Recognit Lett, 27, 861-874, (2006) |
| [56] | Ferguson RA, Pratt DN, Turtle PR, MacIntyre IB, Moore DP, Kearney PD, Best MJ, Gardner JL, Berman M, Buckley MJ (2003) Road pavement deterioration inspection system. Google Patents |
| [57] | Fernandez-Leon, JA; Acosta, GG; Rozenfeld, A, How simple autonomous decisions evolve into robust behaviours? A review from neurorobotics, cognitive, self-organized and artificial immune systems fields, Biosystems, 124, 7-20, (2014) |
| [58] | Files, BT; Marathe, AR, A regression method for estimating performance in a rapid serial visual presentation target detection task, J Neurosci Methods, 258, 114-123, (2016) |
| [59] | Foithong, S; Pinngern, O; Attachoo, B, Feature subset selection wrapper based on mutual information and rough sets, Exp Syst Appl, 39, 574-584, (2012) |
| [60] | Gao, X; Deng, X; Chen, N; Luo, W; Hu, L; Jackson, T; Chen, H, Attentional biases among body-dissatisfied Young women: an ERP study with rapid serial visual presentation, Int J Psychophysiol, 82, 133-142, (2011) |
| [61] | Gavilán, M; Balcones, D; Marcos, O; Llorca, DF; Sotelo, MA; Parra, I; Ocaña, M; Aliseda, P; Yarza, P; Amírola, A, Adaptive road crack detection system by pavement classification, Sensors, 11, 9628-9657, (2011) |
| [62] | Gerson, AD; Parra, LC; Sajda, P, Cortically coupled computer vision for rapid image search, IEEE Trans Neural Syst Rehabil Eng, 14, 174-179, (2006) |
| [63] | Golparvar-Fard, M; Balali, V; Garza, JM, Segmentation and recognition of highway assets using image-based 3D point clouds and semantic texton forests, J Comput Civ Eng, 29, 04014023, (2015) |
| [64] | Grandsaert PJ (2015) Integrating pavement crack detection and analysis using autonomous unmanned aerial vehicle imagery. DTIC Document |
| [65] | Graves SW (2013) Electro-optical sensor evaluation of airfield pavement. In: Airfield and highway pavement 2013: sustainable and efficient pavements—proceedings of the 2013 airfield and highway pavement conference |
| [66] | Guan, H; Li, J; Yu, Y; Chapman, M; Wang, C, Automated road information extraction from mobile laser scanning data, IEEE Trans Intell Transp Syst, 16, 194-205, (2015) |
| [67] | Guan, H; Li, J; Yu, Y; Chapman, M; Wang, H; Wang, C; Zhai, R, Iterative tensor voting for pavement crack extraction using mobile laser scanning data, IEEE Trans Geosci Remote Sens, 53, 1527-1537, (2014) |
| [68] | Guenard, A; Ciarletta, L, The AETOURNOS project: using a flock of UAVs as a cyber physical system and platform for application-driven research, Proced Comput Sci, 10, 939-945, (2012) |
| [69] | Guo, W; Soibelman, L; Garrett, JH, Automated defect detection for sewer pipeline inspection and condition assessment, Autom Constr, 18, 587-596, (2009) |
| [70] | Haas RCG, Hudson WR, Falls LC (2015) Pavement asset management. Wiley, Hoboken |
| [71] | Hall MA (1999) Correlation-based feature selection for machine learning. The University of Waikato, Hamilton |
| [72] | Han, J; Sun, Z; Hao, H, Selecting feature subset with sparsity and low redundancy for unsupervised learning, Knowl Based Syst, 86, 210-223, (2015) |
| [73] | Han, Y; Yang, Y; Yan, Y; Ma, Z; Sebe, N; Zhou, X, Semisupervised feature selection via spline regression for video semantic recognition, IEEE Trans Neural Netw Learn Syst, 26, 252-264, (2015) |
| [74] | Hsu, H-H; Hsieh, C-W; Lu, M-D, Hybrid feature selection by combining filters and wrappers, Exp Syst Appl, 38, 8144-8150, (2011) |
| [75] | Huang, J-J; Cai, Y-Z; Xu, X-M, A parameterless feature ranking algorithm based on MI, Neurocomputing, 71, 1656-1668, (2008) |
| [76] | Huang, J; Cai, Y; Xu, X, A hybrid genetic algorithm for feature selection wrapper based on mutual information, Pattern Recognit Lett, 28, 1825-1844, (2007) |
| [77] | Huang, J; Liu, W; Sun, X, A pavement crack detection method combining 2D with 3D information based on Dempster-Shafer theory, Comput Aided Civ Infrastruct Eng, 29, 299-313, (2014) |
| [78] | Huang W, Zhang N (2012) A novel road crack detection and identification method using digital image processing techniques. In: 2012 7th International conference on computing and convergence technology (ICCIT, ICEI and ICACT), ICCCT 2012. Seoul |
| [79] | Huang, Y; Tsai, YJ, Dynamic programming and connected component analysis for an enhanced pavement distress segmentation algorithm, Transp Res Rec, 2225, 89-98, (2011) |
| [80] | Huang, Y; Xu, B, Automatic inspection of pavement cracking distress, J Electron Imaging, 15, 013017-013017-013016, (2006) |
| [81] | Hubacher, J, The phantom leaf effect: a replication, part 1, J Altern Complement Med, 21, 83-90, (2015) |
| [82] | Huili Z, Guofeng Q, Xingjian W (2010) Improvement of canny algorithm based on pavement edge detection. In: 3rd International congress on image and signal processing (CISP), 2010 |
| [83] | Iacoviello, D; Petracca, A; Spezialetti, M; Placidi, G, A real-time classification algorithm for EEG-based BCI driven by self-induced emotions, Comput Methods Progr Biomed, 122, 293-303, (2015) |
| [84] | Immerzeel, WW; Kraaijenbrink, PDA; Shea, JM; Shrestha, AB; Pellicciotti, F; Bierkens, MFP; Jong, SM, High-resolution monitoring of himalayan glacier dynamics using unmanned aerial vehicles, Remote Sens Environ, 150, 93-103, (2014) |
| [85] | Jahanshahi, MR; Jazizadeh, F; Masri, SF; Becerik-Gerber, B, Unsupervised approach for autonomous pavement-defect detection and quantification using an inexpensive depth sensor, J Comput Civ Eng, 27, 743-754, (2012) |
| [86] | Jahanshahi MR, Karimi FJ, Masri SF, Becerik-Gerber B (2013) Autonomous pavement condition assessment. Google Patents |
| [87] | Jahanshahi, MR; Kelly, JS; Masri, SF; Sukhatme, GS, A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures, Struct Infrastruct Eng, 5, 455-486, (2009) |
| [88] | Jahanshahi MR, Masri SF (2011) A novel crack detection approach for condition assessment of structures. In: ASCE international workshop on computing in civil engineering. Miami |
| [89] | Jahanshahi, MR; Masri, SF, Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures, Autom Constr, 22, 567-576, (2012) |
| [90] | Jahanshahi, MR; Masri, SF, A new methodology for non-contact accurate crack width measurement through photogrammetry for automated structural safety evaluation, Smart Mater Struct, 22, 035019, (2013) |
| [91] | Jahanshahi, MR; Masri, SF; Padgett, CW; Sukhatme, GS, An innovative methodology for detection and quantification of cracks through incorporation of depth perception, Mach Vis Appl, 24, 227-241, (2013) |
| [92] | Jain AK (1989) Fundamentals of digital image processing. Prentice-Hall, Inc., Upper Saddle River · Zbl 0744.68134 |
| [93] | Jiang, J; Liu, H; Ye, H; Feng, F, Crack enhancement algorithm based on improved EM, J Inf Comput Sci, 12, 1037-1043, (2015) |
| [94] | Jie, F; Licheng, J; Fang, L; Tao, S; Xiangrong, Z, Mutual-information-based semi-supervised hyperspectral band selection with high discrimination, high information, and low redundancy, IEEE Trans Geosci Remote Sens, 53, 2956-2969, (2015) |
| [95] | Jiexian, Z; Lili, Z; Xiang, F, An improved beamlet tree-structured algorithm and its application in pavement crack detection, Sci Res Essays, 7, 1175-1184, (2012) |
| [96] | Jiménez-González, A; Martinez-de Dios, JR; Ollero, A, Testbeds for ubiquitous robotics: a survey, Robot Auton Syst, 61, 1487-1501, (2013) |
| [97] | Jin C, Liu J, Guo J (2015) A hybrid model based on mutual information and support vector machine for automatic image annotation. Artificial intelligence perspectives and applications. Springer, New York, pp 29-38 |
| [98] | Jin, X; Davis, CH, An integrated system for automatic road mapping from high-resolution multi-spectral satellite imagery by information fusion, Inf Fus, 6, 257-273, (2005) |
| [99] | Jing L, Aiqin Z (2010) Pavement crack distress detection based on image analysis. In: 2010 International conference on machine vision and human-machine interface, MVHI 2010 |
| [100] | Jung, K; Chu, B; Hong, D, Robot-based construction automation: an application to steel beam assembly (part II), Autom Constr, 32, 62-79, (2013) |
| [101] | Kaseko, MS; Lo, Z-P; Ritchie, SG, Comparison of traditional and neural classifiers for pavement-crack detection, J Transp Eng, 120, 552-569, (1994) |
| [102] | Kaseko, MS; Ritchie, SG, A neural network-based methodology for pavement crack detection and classification, Transp Res C Emerg Technol, 1, 275-291, (1993) |
| [103] | Kaul, V; Tsai, Y; Mersereau, RM, Quantitative performance evaluation algorithms for pavement distress segmentation, Transp Res Rec, 2153, 106-113, (2010) |
| [104] | Kaul V, Tsai Y, Yezzi A (2010) Detection of curves with unknown endpoints using minimal path techniques. In: 2010 21st British machine vision conference, BMVC 2010, Aberystwyth, British Machine Vision Association, BMVA |
| [105] | Kaul, V; Yezzi, A; Tsai, YJ, Detecting curves with unknown endpoints and arbitrary topology using minimal paths, IEEE Trans Pattern Anal Mach Intell, 34, 1952-1965, (2012) |
| [106] | Khan, AM; Ravi, S, Image segmentation methods: a comparative study, Int J Soft Comput Eng (IJSCE), 3, 84-92, (2013) |
| [107] | Khoshgoftaar, TM; Gao, K; Napolitano, A; Wald, R, A comparative study of iterative and non-iterative feature selection techniques for software defect prediction, Inf Syst Front, 16, 801-822, (2014) |
| [108] | Kim, BH; Kim, M; Jo, S, Quadcopter flight control using a low-cost hybrid interface with EEG-based classification and eye tracking, Comput Biol Med, 51, 82-92, (2014) |
| [109] | Kim JJ, Lee HD, Yun DG, Sung JG (2007) Development of laser pavement image processing system to enhance existing automated pavement distress detection process. In: Maintenance and rehabilitation of pavements and technological control: proceedings of the 5th international conference, MAIREPAV 2007 |
| [110] | Kim Y-S, Haas CT, Greer R (1998) Man-machine balanced crack sealing process for UT automated road maintenance machine. In: Proceedings of the international conference on applications of advanced technologies in transportation engineering |
| [111] | Kim, YS; Haas, CT, A man-machine balanced rapid object model for automation of pavement crack sealing and maintenance, Can J Civ Eng, 29, 459-474, (2002) |
| [112] | Kim, YS; Haas, CT; Greer, R, Path planning for machine vision assisted, teleoperated pavement crack sealer, J Transp Eng, 124, 137-143, (1998) |
| [113] | Koch C, Brilakis I (2011) Automated detection of potholes in visual data. In: ISEC 2011: 6th international structural engineering and construction conference—modern methods and advances in structural engineering and construction |
| [114] | Koch, C; Georgieva, K; Kasireddy, V; Akinci, B; Fieguth, P, A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure, Adv Eng Inform, 29, 196-210, (2015) |
| [115] | Kostavelis, I; Gasteratos, A, Semantic mapping for mobile robotics tasks: a survey, Robot Auton Syst, 66, 86-103, (2015) |
| [116] | Koutsopoulos, H; Downey, A, Primitive-based classification of pavement cracking images, J Transp Eng, 119, 402-418, (1993) |
| [117] | Koutsopoulos, HN; Sanhouri, I; Downey, AB, Analysis of segmentation algorithms for pavement distress images, J Transp Eng, 119, 868-888, (1993) |
| [118] | Kruglova, T; Sayfeddine, D; Vitaliy, K, Robotic laser inspection of airplane wings using quadrotor, Procedia Eng, 129, 245-251, (2015) |
| [119] | Kumar, V; Chhabra, JK; Kumar, D, Automatic unsupervised feature selection using gravitational search algorithm, IETE J Res, 61, 22-31, (2015) |
| [120] | Kupferberg, A; Glasauer, S; Burkart, JM, Do robots have goals? how agent cues influence action understanding in non-human primates, Behav Brain Res, 246, 47-54, (2013) |
| [121] | Laurent J, Doucet M (2010) Vision system and a method for scanning a traveling surface to detect surface defects thereof. Google Patents |
| [122] | LeBlanc J, Gennert MA, Wittels N, Gosselin D (1991) Analysis and generation of pavement distress images using fractals. Transp Res Rec (1311) |
| [123] | Lee, BJ; Lee, H, Position-invariant neural network for digital pavement crack analysis, Comput Aided Civ Infrastruct Eng, 19, 105-118, (2004) |
| [124] | Lee, J; Kim, D-W, Mutual information-based multi-label feature selection using interaction information, Exp Syst Appl, 42, 2013-2025, (2015) |
| [125] | Leong, BTM; Low, SM; Ooi, MP-L, Low-cost microcontroller-based hover control design of a quadcopter, Procedia Eng, 41, 458-464, (2012) |
| [126] | Lettsome, CA; Tsai, Y, An automated filter bank-based pavement crack detection system incorporating standard compression coders, Int J Pavement Res Technol, 5, 176-186, (2012) |
| [127] | Li, G, Improved pavement distress detection based on contourlet transform and multi-direction morphological structuring elements, Appl Mech Mater, 466-467, 371-375, (2012) |
| [128] | Li G (2012) Improved pavement distress detection based on contour let transform and multi-direction morphological structuring elements. In: 2012 International conference on intelligent system and applied material, GSAM 2012, vol 466. Taiyuan. pp 371-375 |
| [129] | Li, G, New weighted Mean filtering algorithm for surface image based on grey entropy, Sens Transducers, 161, 21-26, (2013) |
| [130] | Li, G; Xu, Y; Li, J, Fuzzy contrast enhancement algorithm for road surface image based on adaptively changing index via grey entropy, Inf Technol J, 12, 5309-5314, (2013) |
| [131] | Li L, Sun LJ, Tan SG, Ning GB (2012) An efficient way in image preprocessing for pavement crack images. In: CICTP 2012: multimodal transportation systems—convenient, safe, cost-effective, efficient—proceedings of the 12th COTA international conference of transportation professionals |
| [132] | Li L, Sun LJ, Tan SG, Ning GB (2012) An efficient way in image preprocessing for pavement crack images. In: 12th COTA international conference of transportation professionals: multimodal transportation systems—convenient, safe, cost-effective, efficient, CICTP 2012. Beijing |
| [133] | Li, M; Stein, A; Bijker, W; Zhan, Q, Region-based urban road extraction from VHR satellite images using binary partition tree, Int J Appl Earth Obs Geoinform, 44, 217-225, (2016) |
| [134] | Li N, Hou X, Yang X, Dong Y (2009) Automation recognition of pavement surface distress based on support vector machine. In: Second international conference on intelligent networks and intelligent systems, 2009. ICINIS’09. IEEE |
| [135] | Li, Q; Zou, Q; Zhang, D; Mao, Q, Fosa: F* seed-growing approach for crack-line detection from pavement images, Image Vis Comput, 29, 861-872, (2011) |
| [136] | Lin J, Liu Y (2010) Potholes detection based on SVM in the pavement distress image. In: Ninth international symposium on distributed computing and applications to business engineering and science (DCABES), 2010 IEEE |
| [137] | Ling, L; Peikang, H; Xiaohu, W; Xudong, P, Image edge detection based on beamlet transform, J Syst Eng Electron, 20, 1-5, (2009) |
| [138] | Liu F, Xu G, Yang Y, Niu X, Pan Y (2008) Novel approach to pavement cracking automatic detection based on segment extending. In: International symposium on knowledge acquisition and modeling, 2008. KAM’08. IEEE |
| [139] | Liu, H; Sun, J; Liu, L; Zhang, H, Feature selection with dynamic mutual information, Pattern Recognit, 42, 1330-1339, (2009) · Zbl 1183.68540 |
| [140] | Lokeshwor, H; Das, LK; Goel, S, Robust method for automated segmentation of frames with/without distress from road surface video clips, J Transp Eng, 140, 31-41, (2013) |
| [141] | Lokeshwor, H; Das, LK; Goel, S, Robust method for automated segmentation of frames with/without distress from road surface video clips, J Transp Eng, 140, 31-41, (2014) |
| [142] | Luo, W; Feng, W; He, W; Wang, N-Y; Luo, Y-J, Three stages of facial expression processing: ERP study with rapid serial visual presentation, NeuroImage, 49, 1857-1867, (2010) |
| [143] | Ma C-X, Zhao C-X, Hou Y-K (2008) Pavement distress detection based on nonsubsampled contourlet transform. In: International conference on computer science and software engineering, 2008, IEEE |
| [144] | Mahler, DS; Kharoufa, ZB; Wong, EK; Shaw, LG, Pavement distress analysis using image processing techniques, Comput Aided Civ Infrastruct Eng, 6, 1-14, (1991) |
| [145] | Maini, R; Aggarwal, H, Study and comparison of various image edge detection techniques, Int J Image Process (IJIP), 3, 1-11, (2009) |
| [146] | Maldonado, S; Carrizosa, E; Weber, R, Kernel penalized K-means: a feature selection method based on kernel K-means, Inf Sci, 322, 150-160, (2015) · Zbl 1390.68545 |
| [147] | Mancini A, Malinverni ES, Frontoni E, Zingaretti P (2013) Road pavement crack automatic detection by MMS images. In: 2013 21st Mediterranean conference on control and automation, MED 2013—conference proceedings |
| [148] | Marques AGCS, Correia PL (2012) Automatic road pavement crack detection using SVM. Lisbon, Portugal: Dissertation for the Master of Science Degree in Electrical and Computer Engineering at Instituto Superior Técnico |
| [149] | Mathavan, S; Kamal, K; Rahman, M, A review of three-dimensional imaging technologies for pavement distress detection and measurements, IEEE Trans Intell Transp Syst, 16, 2353-2362, (2015) |
| [150] | McCane, LM; Heckman, SM; McFarland, DJ; Townsend, G; Mak, JN; Sellers, EW; Zeitlin, D; Tenteromano, LM; Wolpaw, JR; Vaughan, TM, P300-based brain-computer interface (BCI) event-related potentials (ERPs): people with amyotrophic lateral sclerosis (ALS) vs. age-matched controls, Clin Neurophysiol, 126, 2124-2131, (2015) |
| [151] | McCune, RR; Madey, GR, Control of artificial swarms with DDDAS, Procedia Comput Sci, 29, 1171-1181, (2014) |
| [152] | McGhee KH (2004) Automated pavement distress collection techniques. Transp Res Board 334 |
| [153] | McNeil, S; Humplick, F, Evaluation of errors in automated pavement-distress data acquisition, J Transp Eng, 117, 224-241, (1991) |
| [154] | Mertz C, Varadharajan S, Jose S, Sharma K, Wander L, Wang J (2014) City-wide road distress monitoring with smartphones. In: 21st World congress on intelligent transport systems: reinventing transportation in our connected world, ITSWC 2014, intelligent transport systems (ITS) |
| [155] | Metni, N; Hamel, T, A UAV for bridge inspection: visual servoing control law with orientation limits, Autom Constr, 17, 3-10, (2007) |
| [156] | Michaelsen, E; Meidow, J, Stochastic reasoning for structural pattern recognition: an example from image-based UAV navigation, Pattern Recognit, 47, 2732-2744, (2014) |
| [157] | Mitra, P; Murthy, C; Pal, SK, Unsupervised feature selection using feature similarity, IEEE Trans Pattern Anal Mach Intell, 24, 301-312, (2002) |
| [158] | Moazzam I, Kamal K, Mathavan S, Usman S, Rahman M (2013) Metrology and visualization of potholes using the microsoft kinect sensor. In: IEEE conference on intelligent transportation systems, proceedings, ITSC |
| [159] | Nejad, FM; Zakeri, H, A comparison of multi-resolution methods for detection and isolation of pavement distress, Exp Syst Appl, 38, 2857-2872, (2011) |
| [160] | Mohajeri MJH, Manning PJ (1991) Aria (trademark): an operating system of pavement distress diagnosis by image processing. Transp Res Rec 1311 |
| [161] | Mokhtari S (2015) Analytical study of computer vision-based pavement crack quantification using machine learning techniques. University of Central Florida Orlando, Florida |
| [162] | Montero, R; Victores, JG; Martínez, S; Jardón, A; Balaguer, C, Past, present and future of robotic tunnel inspection, Autom Constr, 59, 99-112, (2015) |
| [163] | Moussa G, Hussain K (2011) A new technique for automatic detection and parameters estimation of pavement crack. In: 4th International multi-conference on engineering and technological innovation, IMETI 2011. Orlando · Zbl 1390.68545 |
| [164] | Na W, Tao W (2012) Proximal support vector machine based pavement image classification. In: 2012 IEEE 5th international conference on advanced computational intelligence, ICACI 2012. Nanjing |
| [165] | Naidoo T, Joubert D, Chiwewe T, Tyatyantsi A, Rancati B, Mbizeni A (2014) Visual surveying platform for the automated detection of road surface distresses. In: Proceedings of SPIE: the international society for optical engineering |
| [166] | Natalizio, E; Caro, G; Sekercioglu, A; Yanmaz, E, A special issue of ad hoc networks on “theory, algorithms and applications of wireless networked robotics”, Ad Hoc Netw, 11, 1891-1892, (2013) |
| [167] | Nejad, FM; Zakeri, H, An expert system based on wavelet transform and Radon neural network for pavement distress classification, Exp Syst Appl, 38, 7088-7101, (2011) |
| [168] | Nejad, FM; Zakeri, H, An optimum feature extraction method based on wavelet-Radon transform and dynamic neural network for pavement distress classification, Exp Syst Appl, 38, 9442-9460, (2011) |
| [169] | Nejad FM, Zakeri H (2012) The hybrid method and its application to smart pavement management. Metaheuristics Water Geotech Transp Eng 439 |
| [170] | Nguyen, T; Khosravi, A; Creighton, D; Nahavandi, S, EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems, Exp Syst Appl, 42, 4370-4380, (2015) |
| [171] | Nguyen TS, Avila M, Begot S (2009) Automatic detection and classification of defect on road pavement using anisotropy measure. In: Signal processing conference, 2009 17th European. IEEE, pp 617-621 |
| [172] | Ni Z, Tang P, Xi Y (2012) A new method to pavement cracking detection based on the biological inspired model. In: 2012 International conference on computer science and information processing, CSIP 2012. Xi’an |
| [173] | Nishikawa, T; Yoshida, J; Sugiyama, T; Fujino, Y, Concrete crack detection by multiple sequential image filtering, Comput Aided Civ Infrastruct Eng, 27, 29-47, (2012) |
| [174] | Oh, J-K; Jang, G; Oh, S; Lee, JH; Yi, B-J; Moon, YS; Lee, JS; Choi, Y, Bridge inspection robot system with machine vision, Autom Constr, 18, 929-941, (2009) |
| [175] | Oliveira H, Caeiro J, Correia PL (2014) Accelerated unsupervised filtering for the smoothing of road pavement surface imagery. In: European signal processing conference |
| [176] | Oliveira H, Caeiro JJ, Correia PL (2010) Improved road crack detection based on one-class Parzen density estimation and entropy reduction. In: 2010 17th IEEE international conference on image processing, ICIP 2010. Hong Kong |
| [177] | Oliveira H, Correia PL (2008) Supervised strategies for cracks detection in images of road pavement flexible surfaces. In: Signal processing conference, 2008 16th European, IEEE |
| [178] | Oliveira H, Correia PL (2010) Automatic crack detection on road imagery using anisotropic diffusion and region linkage. In: 18th European signal processing conference, EUSIPCO 2010. Aalborg |
| [179] | Oliveira, H; Correia, PL, Automatic road crack detection and characterization, IEEE Trans Intell Transp Syst, 14, 155-168, (2013) |
| [180] | Oliveira H, Correia PL (2014) CrackIT: an image processing toolbox for crack detection and characterization. In: 2014 IEEE international conference on image processing, ICIP 2014 |
| [181] | Ouyang A, Dong Q, Wang Y, Liu Y (2014) The classification of pavement crack image based on beamlet algorithm. In: 7th IFIP WG 5.14 international conference on computer and computing technologies in agriculture, CCTA 2013 |
| [182] | Ouyang A, Wang Y (2012) Edge detection in pavement crack image with beamlet transform. In: 2012 2nd International conference on electronic and mechanical engineering and information technology, EMEIT 2012. Shenyang |
| [183] | Ouyang, W; Xu, B, Pavement cracking measurements using 3D laser-scan images, Meas Sci Technol, 24, 105204, (2013) |
| [184] | Paraforos, DS; Griepentrog, HW; Vougioukas, SG, Country road and field surface profiles acquisition, modelling and synthetic realisation for evaluating fatigue life of agricultural machinery, J Terramech, 63, 1-12, (2016) |
| [185] | Peng, K; Cai, G; Chen, BM; Dong, M; Lum, KY; Lee, TH, Design and implementation of an autonomous flight control law for a UAV helicopter, Automatica, 45, 2333-2338, (2009) · Zbl 1179.93083 |
| [186] | Prakash S, Chowdhury AR, Gupta A (2015) Monitoring the human health by measuring the biofield “aura”: an overview. Int J Appl Eng Res 10(2765427658) |
| [187] | Pynn J, Wright A, Lodge R (1999) Automatic identification of cracks in road surfaces. In: Image processing and its applications, 1999. Seventh International Conference on (Conf. Publ. No. 465) |
| [188] | Qureshi, B; Koubâa, A, Five traits of performance enhancement using cloud robotics: a survey, Procedia Comput Sci, 37, 220-227, (2014) |
| [189] | Rababaah H (2005) Asphalt pavement crack classification: a comparative study of three ai approaches—multilayer perceptron, genetic algorithms, and self-organizing maps. Citeseer |
| [190] | Rababaah H, Vrajitoru D, Wolfer J (2005) Asphalt pavement crack classification: a comparison of GA, MLP, and SOM. In: Proceedings of genetic and evolutionary computation conference. Late-Breaking Paper |
| [191] | Radopoulou, SC; Brilakis, I, Patch detection for pavement assessment, Autom Constr, 53, 95-104, (2015) |
| [192] | Rai, R; Deshpande, AV, Fragmentary shape recognition: a BCI study, Comput Aided Des, 71, 51-64, (2016) |
| [193] | Raman, M; Hossain, M; Miller, R; Cumberledge, G; Lee, H; Kang, K, Assessment of image-based data collection and the AASHTO provisional standard for cracking on asphalt-surfaced pavements, Transp Res Rec J Transp Res Board, 1889, 116-125, (2004) |
| [194] | Reeves B (2011) High speed photometric stereo pavement scanner. Google Patents |
| [195] | Roca, D; Lagüela, S; Díaz-Vilariño, L; Armesto, J; Arias, P, Low-cost aerial unit for outdoor inspection of building façades, Autom Constr, 36, 128-135, (2013) |
| [196] | Rouillard, J; Duprès, A; Cabestaing, F; Leclercq, S; Bekaert, M-H; Piau, C; Vannobel, J-M; Lecocq, C, Hybrid BCI coupling EEG and EMG for severe motor disabilities, Procedia Manuf, 3, 29-36, (2015) |
| [197] | Saar T, Talvik O (2010) Automatic asphalt pavement crack detection and classification using neural networks. In: Electronics conference (BEC), 2010 12th Biennial Baltic, IEEE |
| [198] | Salari E, Bao G (2010) Pavement distress detection and classification using feature mapping. In: 2010 IEEE international conference on electro/information technology, EIT2010. Normal |
| [199] | Salari E, Bao G (2011) Pavement distress detection and severity analysis. In: Proceedings of SPIE. The International Society for Optical Engineering |
| [200] | Salari E, Ouyang D (2012) An image-based pavement distress detection and classification. In: 2012 IEEE international conference on electro/information technology, EIT 2012. Indianapolis |
| [201] | Salari E, Yu X (2011) Pavement distress detection and classification using a Genetic Algorithm. In: Applied imagery pattern recognition workshop (AIPR), 2011 IEEE, IEEE |
| [202] | Salman M, Mathavan S, Kamal K, Rahman M (2013) Pavement crack detection using the Gabor filter. In: 2013 16th international IEEE conference on intelligent transportation systems: intelligent transportation systems for all modes, ITSC 2013. The Hague |
| [203] | Schnebele, E; Tanyu, BF; Cervone, G; Waters, N, Review of remote sensing methodologies for pavement management and assessment, Eur Transp Res Rev, 7, 1-19, (2015) |
| [204] | Senanayake, M; Senthooran, I; Barca, JC; Chung, H; Kamruzzaman, J; Murshed, M, Search and tracking algorithms for swarms of robots: a survey, Robot Auton Syst, 75, 422-434, (2016) |
| [205] | Senthilkumaran N, Rajesh R (2009) Edge detection techniques for image segmentation-a survey of soft computing approaches. Int J Recent Trends Eng 1(2) |
| [206] | Senthilkumaran N, Rajesh R (2009) Image segmentation-a survey of soft computing approaches. In: International conference on advances in recent technologies in communication and computing, 2009. ARTCom’09. IEEE, pp 844-846 |
| [207] | Sharifi M, Fathy M, Mahmoudi MT (2002) A classified and comparative study of edge detection algorithms. In: Proceedings international conference on information technology: coding and computing, 2002. IEEE |
| [208] | Shen, Y; Dang, JW; Wang, YP; Feng, X, A compressed sensing pavement distress image filtering algorithm based on NSCT domain, J Optoelectron Laser, 25, 1620-1626, (2014) |
| [209] | Shi, C; Ruan, Q; An, G; Zhao, R, Hessian semi-supervised sparse feature selection based on-matrix norm, IEEE Trans Multimed, 17, 16-28, (2015) |
| [210] | Shukla, A; Karki, H, Application of robotics in offshore oil and gas industry: a review part II, Robot Auton Syst, 75, 508-524, (2016) |
| [211] | Shukla, A; Karki, H, Application of robotics in onshore oil and gas industry: a review part I, Robot Auton Syst, 75, 490-507, (2016) |
| [212] | Siebert, S; Teizer, J, Mobile 3D mapping for surveying earthwork projects using an unmanned aerial vehicle (UAV) system, Autom Constr, 41, 1-14, (2014) |
| [213] | Solla, M; Lagüela, S; González-Jorge, H; Arias, P, Approach to identify cracking in asphalt pavement using GPR and infrared thermographic methods: preliminary findings, NDT E Int, 62, 55-65, (2014) |
| [214] | Song, B; Wei, N, Statistics properties of asphalt pavement images for cracks detection, J Inf Comput Sci, 10, 2833-2843, (2013) |
| [215] | Song, H; Wang, W; Wang, F; Wu, L; Wang, Z, Pavement crack detection by ridge detection on fractional calculus and dual-thresholds, Int J Multimed Ubiquitous Eng, 10, 19-30, (2015) |
| [216] | Sridevi, M; Mala, C, A survey on monochrome image segmentation methods, Procedia Technol, 6, 548-555, (2012) |
| [217] | Stamatescu V, Wong S, Kearney D, Lee I, Milton A (2015) Mutual information for enhanced feature selection in visual tracking. In: SPIE defense + security. International Society for Optics and Photonics |
| [218] | Su, YS; Kang, SC; Chang, JR; Hsieh, SH, Dual-light inspection method for automatic pavement surveys, J Comput Civ Eng, 27, 534-543, (2013) |
| [219] | Sun, X; Huang, J; Liu, W; Xu, M, Pavement crack characteristic detection based on sparse representation, EURASIP J Adv Signal Process, 2012, 1-11, (2012) |
| [220] | Sun, ZY; Hao, XL; Li, W; Yuan, MX, Research of pavement 3D data denoising algorithm, J Chang Univ (Nat Sci Ed), 35, 20-25, (2015) |
| [221] | Sun, ZY; Zhao, HW; Li, W; Hao, XL; Huyan, J, 3D pavement crack identification method based on dual-phase scanning detection, China J Highway Transp, 28, 26-32, (2015) |
| [222] | Sundin, S; Braban-Ledoux, C, Artificial intelligence-based decision support technologies in pavement management, Comput Aided Civ Infrastruct Eng, 16, 143-157, (2001) |
| [223] | Tan, Y; Zheng, Z-Y, Research advance in swarm robotics, Def Technol, 9, 18-39, (2013) |
| [224] | Tang J, Gu Y (2013) Automatic crack detection and segmetnation using a hybrid algorithm for road distress analysis. In: 2013 IEEE international conference on systems, man, and cybernetics, SMC 2013. Manchester |
| [225] | Terzi, S, Modeling for pavement roughness using the ANFIS approach, Adv Eng Softw, 57, 59-64, (2013) |
| [226] | Timm DH, McQueen JM (2004) A study of manual vs. automated pavement condition surveys. Auburn University, Alabama |
| [227] | Tomiyama, K; Kawamura, A; Ishida, T, Automatic detection method of localized pavement roughness using quarter car model by lifting wavelet filters, Int J Pavement Res Technol, 6, 627-632, (2013) |
| [228] | Tong, H; Chao, WW; Qiang, HC; Bo, XY, Path planning of UAV based on Voronoi diagram and DPSO, Procedia Eng, 29, 4198-4203, (2012) |
| [229] | Tsai, Y-C; Kaul, V; Mersereau, RM, Critical assessment of pavement distress segmentation methods, J Transp Eng, 136, 11-19, (2009) |
| [230] | Tsai, Y; Kaul, V; Yezzi, A, Automating the crack map detection process for machine operated crack sealer, Autom Constr, 31, 10-18, (2013) |
| [231] | Tsai, YC; Jiang, C; Huang, Y, Multiscale crack fundamental element model for real-world pavement crack classification, J Comput Civ Eng, 28, 04014012, (2014) |
| [232] | Tsai, YC; Kaul, V; Lettsome, CA, Enhanced adaptive filter-bank-based automated pavement crack detection and segmentation system, J Electron Imaging, 21, 043008-043008, (2012) |
| [233] | Tsai, YC; Kaul, V; Mersereau, RM, Critical assessment of pavement distress segmentation methods, J Transp Eng, 136, 11-19, (2010) |
| [234] | Tsai, YJ; Li, F, Critical assessment of detecting asphalt pavement cracks under different lighting and low intensity contrast conditions using emerging 3D laser technology, J Transp Eng, 138, 649-656, (2012) |
| [235] | Tseng, Y-H; Kang, S-C; Chang, J-R; Lee, C-H, Strategies for autonomous robots to inspect pavement distresses, Autom Constr, 20, 1156-1172, (2011) |
| [236] | Uncu, Ö; Türkşen, IB, A novel feature selection approach: combining feature wrappers and filters, Inf Sci, 177, 449-466, (2007) · Zbl 1142.68494 |
| [237] | Varadharajan S, Jose S, Sharma K, Wander L, Mertz C (2014) Vision for road inspection. In: IEEE winter conference on applications of computer vision (WACV), 2014 IEEE |
| [238] | Varela, G; Caamaño, P; Orjales, F; Deibe, Á; López-Peña, F; Duro, RJ, Autonomous UAV based search operations using constrained sampling evolutionary algorithms, Neurocomputing, 132, 54-67, (2014) |
| [239] | Vasuki, Y; Holden, E-J; Kovesi, P; Micklethwaite, S, Semi-automatic mapping of geological structures using UAV-based photogrammetric data: an image analysis approach, Comput Geosci, 69, 22-32, (2014) |
| [240] | Velinsky, SA; Feng, X; Bennett, DA, Operator controlled, vehicle-based highway crack-sealing machine, Heavy Veh Syst, 10, 145-166, (2003) |
| [241] | Verma, SK; Chauhan, R, Nanorobotics in dentistry: a review, Indian J Dent, 5, 62-70, (2014) |
| [242] | Victores, JG; Martínez, S; Jardón, A; Balaguer, C, Robot-aided tunnel inspection and maintenance system by vision and proximity sensor integration, Autom Constr, 20, 629-636, (2011) |
| [243] | Wang C, Sha A, Sun Z (2010) Pavement crack classification based on chain code. In: Seventh international conference on fuzzy systems and knowledge discovery (FSKD), 2010, IEEE |
| [244] | Wang, J; Gao, RX, Pavement distress analysis based on dual-tree complex wavelet transform, Int J Pavement Res Technol, 5, 283-288, (2012) |
| [245] | Wang, JJ-Y; Huang, JZ; Sun, Y; Gao, X, Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization, Exp Syst Appl, 42, 1278-1286, (2015) |
| [246] | Wang, K, Designs and implementations of automated systems for pavement surface distress survey, J Infrastruct Syst, 6, 24-32, (2000) |
| [247] | Wang, K; Li, Q; Gong, W, Wavelet-based pavement distress image edge detection with a trous algorithm, Transp Res Rec J Transp Res Board, 2024, 73-81, (2008) |
| [248] | Wang, KCP; Li, Q; Gong, W, Wavelet-based pavement distress image edge detection with a trous algorithm, Transp Res Rec, 2024, 73-81, (2007) |
| [249] | Wang, S; Pedrycz, W; Zhu, Q; Zhu, W, Unsupervised feature selection via maximum projection and minimum redundancy, Knowl Based Syst, 75, 19-29, (2015) |
| [250] | Wang S, Tang W (2011) Pavement crack segmentation algorithm based on local optimal threshold of cracks density distribution. In: 7th International conference on intelligent computing, ICIC 2011. Zhengzhou. 6838 LNCS, pp 298-302 |
| [251] | Wang S, Tang W (2011) Pavement crack segmentation algorithm based on local optimal threshold of cracks density distribution. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). 6838 LNCS, pp 298-302 |
| [252] | Wang X, Feng X (2011) Pavement distress detection and classification with automated image processing. In: Proceedings 2011 international conference on transportation, mechanical, and electrical engineering, TMEE 2011 |
| [253] | Wang Z (2000) Formulation and assessment of a customizable procedure for pavement distress index |
| [254] | Wang, Z; Li, M; Li, J, A multi-objective evolutionary algorithm for feature selection based on mutual information with a new redundancy measure, Inf Sci, 307, 73-88, (2015) · Zbl 1387.68200 |
| [255] | Wei, H; Li, N; Liu, M; Tan, J, A novel autonomous self-assembly distributed swarm flying robot, Chin J Aeronaut, 26, 791-800, (2013) |
| [256] | Wei N, Zhao X, Dou X, Song H, Wang T (2010) Beamlet transform based pavement image crack detection. In: International conference on intelligent computation technology and automation (ICICTA), 2010, IEEE |
| [257] | Wirkner, J; Löw, A; Hamm, AO; Weymar, M, New learning following reactivation in the human brain: targeting emotional memories through rapid serial visual presentation, Neurobiol Learn Memory, 119, 63-68, (2015) |
| [258] | Wu C, Lu B, Chen D, Wang L (2011) Pavement image denoising based on shearlet treansform. In: International conference on electronics and optoelectronics (ICEOE), 2011, IEEE |
| [259] | Wu S, Liu Y (2012) A segment algorithm for crack dection. In: 2012 IEEE symposium on electrical and electronics engineering, EEESYM 2012. Kuala Lumpur |
| [260] | Xiao, W-X; Zhang, X; Huang, W, Preliminary study of pavement surface distress automation recognition based on wavelet neural network, Shanghai Highw, 2, 008, (2004) |
| [261] | Xu G, Ma J, Liu F, Niu X (2008) Automatic recognition of pavement surface crack based on Bp neural network. In: International conference on computer and electrical engineering, 2008. ICCEE 2008. IEEE |
| [262] | Xu K, Wei N, Ma R (2013) Pavement crack image detection algorithm under nonuniform illuminance. In: 2013 IEEE 3rd international conference on information science and technology, ICIST 2013, Yangzhou, Jiangsu, IEEE Computer Society |
| [263] | Xu, W; Tang, Z; Xu, D; Wu, G, Integrating multi-features fusion and gestalt principles for pavement crack detection, J Comput Aided Des Comput Graph, 27, 147-156, (2015) |
| [264] | Xu W, Tang Z, Zhou J, Ding J (2013) Pavement crack detection based on saliency and statistical features. In: 2013 20th IEEE international conference on image processing, ICIP 2013. Melbourne |
| [265] | Yan, WY; Shaker, A; El-Ashmawy, N, Urban land cover classification using airborne lidar data: a review, Remote Sens Environ, 158, 295-310, (2015) |
| [266] | Yao, J; Mao, Q; Goodison, S; Mai, V; Sun, Y, Feature selection for unsupervised learning through local learning, Pattern Recognit Lett, 53, 100-107, (2015) |
| [267] | Yao, M; Zhao, Z; Yao, X; Xu, B, Fusing complementary images for pavement cracking measurements, Meas Sci Technol, 26, 025005, (2015) |
| [268] | Yao, P; Wang, H; Su, Z, UAV feasible path planning based on disturbed fluid and trajectory propagation, Chin J Aeronaut, 28, 1163-1177, (2015) |
| [269] | Yassi, M; Moattar, MH, Robust and stable feature selection by integrating ranking methods and wrapper technique in genetic data classification, Biochem Biophys Res Commun, 446, 850-856, (2014) |
| [270] | Ying, H; Zhu, Q; Tan, Z; Wei, J, Faulting estimation method based on vertical acceleration of cement concrete pavement, J Harbin Inst Technol, 45, 96-100, (2013) |
| [271] | Ying L (2009) Beamlet transform based technique for pavement image processing and classification. The University of Toledo, Toledo |
| [272] | Ying, L; Salari, E, Beamlet transform-based technique for pavement crack detection and classification, Comput Aided Civ Infrastruct Eng, 25, 572-580, (2010) |
| [273] | Yoo, H-S; Kim, Y-S, Development of a crack recognition algorithm from non-routed pavement images using artificial neural network and binary logistic regression, KSCE J Civ Eng, 20, 1-12, (2015) |
| [274] | Youquan H, Hanxing Q, Jian W, Wei Z, Jianfang X (2011) Studying of road crack image detection method based on the mathematical morphology. In: 4th International congress on image and signal processing, CISP 2011. Shanghai |
| [275] | Yu, S-N; Jang, J-H; Han, C-S, Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel, Autom Constr, 16, 255-261, (2007) |
| [276] | Yuwono, M; Guo, Y; Wall, J; Li, J; West, S; Platt, G; Su, SW, Unsupervised feature selection using swarm intelligence and consensus clustering for automatic fault detection and diagnosis in heating ventilation and air conditioning systems, Appl Soft Comput, 34, 402-425, (2015) |
| [277] | Zakeri H, Nejad FM, Fahimifar A, Torshizi AD, Zarandi MHF (2013) A multi-stage expert system for classification of pavement cracking. In: IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS), 2013 Joint |
| [278] | Zakeri H, Nejad FM, Fahimifar A, Torshizi AD, Zarandi MHF (2014) A new automatic MF generator (AMFG) for general 3D type-ii fuzzy in the polar frame. In: 2014 IEEE conference on norbert wiener in the 21st century: driving technology’s future, 21CW 2014—incorporating the proceedings of the 2014 North American fuzzy information processing society conference, NAFIPS 2014, conference proceedings |
| [279] | Zalama, E; Gómez-García-Bermejo, J; Medina, R; Llamas, J, Road crack detection using visual features extracted by Gabor filters, Comput Aided Civ Infrastruct Eng, 29, 342-358, (2014) |
| [280] | Zarco-Tejada, PJ; González-Dugo, V; Berni, JAJ, Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera, Remote Sens Environ, 117, 322-337, (2012) |
| [281] | Zarrinpanjeh, N; Samadzadegan, F; Schenk, T, A new ant based distributed framework for urban road map updating from high resolution satellite imagery, Comput Geosci, 54, 337-350, (2013) |
| [282] | Zhang, A; Li, Q; Wang, KCP; Qiu, S, Matched filtering algorithm for pavement cracking detection, Transp Res Rec, 2367, 30-42, (2013) |
| [283] | Zhang C-K, Hu H (2005) Feature selection using the hybrid of ant colony optimization and mutual information for the forecaster. In: Proceedings of 2005 international conference on machine learning and cybernetics, 2005. IEEE |
| [284] | Zhang, C; Elaksher, A, An unmanned aerial vehicle-based imaging system for 3D measurement of unpaved road surface distresses1, Comput Aided Civ Infrastruct Eng, 27, 118-129, (2012) |
| [285] | Zhang, D-Q; Qu, S-R; Li, W-B; He, L, Image enhancement algorithm on ridgelet domain in detection of road cracks, China J Highway Transp, 22, 26-31, (2009) |
| [286] | Zhang, D; Qu, S; He, L; Shi, S, Automatic ridgelet image enhancement algorithm for road crack image based on fuzzy entropy and fuzzy divergence, Opt Lasers Eng, 47, 1216-1225, (2009) |
| [287] | Zhang, H; Fritts, JE; Goldman, SA, Image segmentation evaluation: a survey of unsupervised methods, Comput Vis Image Underst, 110, 260-280, (2008) |
| [288] | Zhang J, Sha A, Sun Z, Gao H (2009). Pavement crack automatic recognition based on wiener filtering. In: Critical issues in transportation system planning, development, and management proceedings of the ninth international conference of chinese transportation professionals · Zbl 1188.94004 |
| [289] | Zhang, Y; Zhou, H, Automatic pavement cracks detection and classification using Radon transform, J Inf Comput Sci, 9, 5241-5247, (2012) |
| [290] | Zhao, J; Lu, K; He, X, Locality sensitive semi-supervised feature selection, Neurocomputing, 71, 1842-1849, (2008) |
| [291] | Zhao Z, Liu H (2007) Semi-supervised feature selection via spectral analysis. SDM, SIAM |
| [292] | Zhibiao, S; Yanqing, G, Algorithm on contourlet domain in detection of road cracks for pavement images, J Algorithms Comput Technol, 7, 15-26, (2013) · Zbl 1280.94021 |
| [293] | Zhou H, Yang S, Zhu J (2010) Illumination invariant enhancement and threshold segmentation algorithm for asphalt pavement crack image. In: 2010 6th International conference on wireless communications, networking and mobile computing, WiCOM 2010. Chengdu |
| [294] | Zhou, HL; Jiang, YL; Wan, X, Evolving fuzzy neural network for highway subsurface condition evaluation using ground penetrating radar, Adv Inf Sci Serv Sci, 3, 176-182, (2011) |
| [295] | Zhou, J; Huang, P; Chiang, F-P, Wavelet-based pavement distress classification, Transp Res Rec J Transp Res Board, 1940, 89-98, (2005) |
| [296] | Zhou, J; Huang, PS; Chiang, F-P, Wavelet-based pavement distress detection and evaluation, Opt Eng, 45, 027007-027007-027010, (2006) |
| [297] | Zhu, Z; German, S; Brilakis, I, Detection of large-scale concrete columns for automated bridge inspection, Autom Constr, 19, 1047-1055, (2010) |
| [298] | Zou, Q; Cao, Y; Li, Q; Mao, Q; Wang, S, Cracktree: automatic crack detection from pavement images, Pattern Recognit Lett, 33, 227-238, (2012) |
| [299] | Zuo YX, Wang GQ, Zuo CC (2013) The segmentation algorithm for pavement cracking images based on the improved fuzzy clustering. In: 2013 2nd International conference on chemical, mechanical and materials engineering, CMME 2013, vol 319. Melbourne. pp 362-366 |
| [300] | 长安大学 (2011) Pavement crack detection system based on image and detection method thereof. Google Patents |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
