Table of Contents Author Guidelines Submit a Manuscript
International Journal of Geophysics
Volume 2011, Article ID 989354, 20 pages
http://dx.doi.org/10.1155/2011/989354
Research Article

Automatic Road Pavement Assessment with Image Processing: Review and Comparison

Departement of MACS, Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR), 44341 Bouguenais Cedex, France

Received 31 January 2011; Revised 21 May 2011; Accepted 6 June 2011

Academic Editor: Jean Dumoulin

Copyright © 2011 Sylvie Chambon and Jean-Marc Moliard. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [91 citations]

The following is the list of published articles that have cited the current article.

  • Zhili Chen, O.O. Olatubosun, Hui Zhang, and Renjie Sun, “Automatic detection of asphalt layer thickness based on Ground Penetrating Radar,” 2016 2nd IEEE International Conference on Computer and Communications (ICCC), pp. 2850–2854, . View at Publisher · View at Google Scholar
  • Roberto Medina, Jose Llamas, Eduardo Zalama, and Jaime Gomez-Garcia-Bermejo, “Enhanced automatic detection of road surface cracks by combining 2D/3D image processing techniques,” 2014 IEEE International Conference on Image Processing (ICIP), pp. 778–782, . View at Publisher · View at Google Scholar
  • Rabih Amhaz, Sylvie Chambon, Jerome Idier, and Vincent Baltazart, “A new minimal path selection algorithm for automatic crack detection on pavement images,” 2014 IEEE International Conference on Image Processing (ICIP), pp. 788–792, . View at Publisher · View at Google Scholar
  • Kelwin Fernandes, and Lucian Ciobanu, “Pavement pathologies classification using graph-based features,” 2014 IEEE International Conference on Image Processing (ICIP), pp. 793–797, . View at Publisher · View at Google Scholar
  • Henrique Oliveira, and Paulo Lobato Correia, “CrackIT — An image processing toolbox for crack detection and characterization,” 2014 IEEE International Conference on Image Processing (ICIP), pp. 798–802, . View at Publisher · View at Google Scholar
  • Jacob Konig, Mark David Jenkins, Peter Barrie, Mike Mannion, and Gordon Morison, “A Convolutional Neural Network for Pavement Surface Crack Segmentation Using Residual Connections and Attention Gating,” 2019 IEEE International Conference on Image Processing (ICIP), pp. 1460–1464, . View at Publisher · View at Google Scholar
  • Markus Eisenbach, Ronny Stricker, Daniel Seichter, Karl Amende, Klaus Debes, Maximilian Sesselmann, Dirk Ebersbach, Ulrike Stoeckert, and Horst-Michael Gross, “How to get pavement distress detection ready for deep learning? A systematic approach,” 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2039–2047, . View at Publisher · View at Google Scholar
  • Shreedhar Savant Todkar, Cedric Le Bastard, Amine Ihamouten, Vincent Baltazart, Xavier Derobert, Cyrille Fauchard, David Guilbert, and Frederic Bosc, “Detection of debondings with Ground Penetrating Radar using a machine learning method,” 2017 9th International Workshop on Advanced Ground Penetrating Radar (IWAGPR), pp. 1–6, . View at Publisher · View at Google Scholar
  • Kenta Urano, Kei Hiroi, Shinpei Kato, Nozomi Komagata, and Nobuo Kawaguchi, “Road Surface Condition Inspection Using a Laser Scanner Mounted on an Autonomous Driving Car,” 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 826–831, . View at Publisher · View at Google Scholar
  • Sindhu Ghanta, Salar Shahini Shamsabadi, Jennifer Dy, Ming Wang, and Ralf Birken, “A Hessian-based methodology for automatic surface crack detection and classification from pavement images,” Structural Health Monitoring and Inspection of Advanced Materials, Aerospace, and Civil Infrastructure 2015, vol. 9437, pp. 94371Z, . View at Publisher · View at Google Scholar
  • V. Baltazart, Ph. Nicolle, and L. Yang, “Ongoing tests and improvements of the MPS algorithm for the automatic crack detection within grey level pavement images,” 2017 25th European Signal Processing Conference (EUSIPCO), pp. 2016–2020, . View at Publisher · View at Google Scholar
  • V. Baltazart, L. Yang, Ph. Nicolle, and J-M. Moliard, “Pseudo-ground truth data collection on pavement images,” 2017 25th European Signal Processing Conference (EUSIPCO), pp. 2021–2025, . View at Publisher · View at Google Scholar
  • Yi-Chang James Tsai, Anirban Chatterjee, and Chenglong Jiang, “Challenges and lessons from the successful implementation of automated road condition surveys on a large highway system,” 2017 25th European Signal Processing Conference (EUSIPCO), pp. 2031–2035, . View at Publisher · View at Google Scholar
  • N. Hassan, S. Mathavan, and K. Kamal, “Road crack detection using the particle filter,” 2017 23rd International Conference on Automation and Computing (ICAC), pp. 1–6, . View at Publisher · View at Google Scholar
  • Adhiguna Mahendra, Christophe Stolz, Fabrice Meriaudeau, Sebastien Petit, Alexandre Noel, and Fabien Degoutin, “Automated inspection of tubular material based on magnetic particle inspection,” Proceedings of SPIE - The International Society for Optical Engineering, vol. 8300, 2012. View at Publisher · View at Google Scholar
  • Eduardo Zalama, Jaime Gómez-García-Bermejo, Roberto Medina, and José Llamas, “Road Crack Detection Using Visual Features Extracted by Gabor Filters,” Computer-Aided Civil and Infrastructure Engineering, 2013. View at Publisher · View at Google Scholar
  • Jianping Huang, Wanyu Liu, and Xiaoming Sun, “A Pavement Crack Detection Method Combining 2D with 3D Information Based on Dempster-Shafer Theory,” Computer-Aided Civil and Infrastructure Engineering, 2013. View at Publisher · View at Google Scholar
  • Henrique Oliveira, and Paulo Lobato Correia, “Automatic road crack detection and characterization,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 1, pp. 155–168, 2013. View at Publisher · View at Google Scholar
  • Zakeri, F. Moghadas Nejad, Fahimifar, A. Doostparast Torshizi, and M. H. Fazel Zarandi, “A multi-stage expert system for classification of pavement cracking,” Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013, pp. 1125–1130, 2013. View at Publisher · View at Google Scholar
  • Fereidoon Moghadas Nejad, and Hamzeh Zakeripp. 439–484, 2013. View at Publisher · View at Google Scholar
  • Karim Hammoudi, John McDonald, Karim Hammoudi, and John McDonald, “Design, implementation and simulation of an experimental multi-camera imaging system for terrestrial and multi-purpose mobile mapping platforms: A case study,” Applied Mechanics and Materials, vol. 332, pp. 139–144, 2013. View at Publisher · View at Google Scholar
  • Nazre Batool, and Rama Chellappa, “Fast Detection of Facial Wrinkles based on Gabor Features using Image Morphology and Geometric Constraints,” Pattern Recognition, 2014. View at Publisher · View at Google Scholar
  • Xavier Gibert, Vishal M. Patel, Demetrio Labate, and Rama Chellappa, “Discrete shearlet transform on GPU with applications in anomaly detection and denoising,” Eurasip Journal on Advances in Signal Processing, 2014. View at Publisher · View at Google Scholar
  • S. Mathavan, M. Rahman, and K. Kamal, “Use of a Self-Organizing Map for Crack Detection in Highly Textured Pavement Images,” Journal of Infrastructure Systems, pp. 04014052, 2014. View at Publisher · View at Google Scholar
  • Kyriacos Themistocleous, Kyriacos Neocleous, Kypros Pilakoutas, and Diofantos G. Hadjimitsis, “Damage assessment using advanced non-intrusive inspection methods: Integration of Space, UAV, GPR and Field Spectroscopy,” Second International Conference on Remote Sensing and Geoinformation of The, vol. 9229, 2014. View at Publisher · View at Google Scholar
  • Sobhagya Jose, Christoph Mertz, Srivatsan Varadharajan, Karan Sharma, and Lars Wander, “Vision for road inspection,” 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, pp. 115–122, 2014. View at Publisher · View at Google Scholar
  • Guerrieri, and Corriere, “A novel technique for monitoring the W-beam guardrails,” Advanced Materials Research, vol. 988, pp. 185–190, 2014. View at Publisher · View at Google Scholar
  • Andrzej Pozarycki, Pawel Sniatala, Andrzej Rybarczyk, Adam Bloch, Rafal Kapela, Adam Turkot, Pawel Rydzewski, and Michal Wyczalek, “Asphalt surfaced pavement cracks detection based on histograms of oriented gradients,” Proceedings of the 22nd International Conference Mixed Design of Integrated Circuits and Systems, MIXDES 2015, pp. 579–584, 2015. View at Publisher · View at Google Scholar
  • Lu Sun, Mojtaba Kamaliardakani, and Yongming Zhang, “Weighted Neighborhood Pixels Segmentation Method for Automated Detection of Cracks on Pavement Surface Images,” Journal of Computing in Civil Engineering, pp. 04015021, 2015. View at Publisher · View at Google Scholar
  • E. Schnebele, B. F. Tanyu, G. Cervone, and N. Waters, “Review of remote sensing methodologies for pavement management and assessment,” European Transport Research Review, vol. 7, no. 2, 2015. View at Publisher · View at Google Scholar
  • Emanuel Aldea, and Sylvie Le Hégarat-Mascle, “Robust crack detection for unmanned aerial vehicles inspection in an a-contrario decision framework,” Journal of Electronic Imaging, vol. 24, no. 6, 2015. View at Publisher · View at Google Scholar
  • Slamet Riyadi, Aris Sugiarto, Atmaja Putra, and Noor Akhmad Setiawan, “Analysis of Digital Image Using Pyramidal Gaussian Method to Detect Pavement Crack,” Advanced Science Letters, vol. 21, no. 11, pp. 3565–3568, 2015. View at Publisher · View at Google Scholar
  • Emanuel Aldea, Sylvie le Hegarat, Emanuel Aldea, and Sylvie le Hegarat, “Robust crack detection strategies for aerial inspection,” Twelfth International Conference On Quality Control By Artificial Vision, vol. 9534, 2015. View at Publisher · View at Google Scholar
  • Marc Franch, Cristiano Silva, Gil Lopes, Fernando Ribeiro, Paulo Trigueiros, Luis Seco, and Neftalí Sillero, “Where to look when identifying roadkilled amphibians?,” Acta Herpetologica, vol. 10, no. 2, pp. 103–110, 2015. View at Publisher · View at Google Scholar
  • Kazuhiko Murasaki, Kyoko Sudo, and Yukinobu Taniguchi, “Manhole Cover Wearing Detection by Photo-taking,” Transactions of the Society of Instrument and Control Engineers, vol. 51, no. 12, pp. 814–821, 2015. View at Publisher · View at Google Scholar
  • Senthan Mathavan, Akash Kumar, Khurram Kamal, Mujib Rahman, Michael Nieminen, and Hitesh Shah, “Fast segmentation of industrial quality pavement images using Laws texture energy measures and k-means clustering,” Journal of Electronic Imaging, vol. 25, no. 5, 2016. View at Publisher · View at Google Scholar
  • H. Zakeri, Fereidoon Moghadas Nejad, and Ahmad Fahimifar, “Image Based Techniques for Crack Detection, Classification and Quantification in Asphalt Pavement: A Review,” Archives of Computational Methods in Engineering, 2016. View at Publisher · View at Google Scholar
  • H. Zakeri, Fereidoon Moghadas Nejad, and Ahmad Fahimifar, “Rahbin: A quadcopter unmanned aerial vehicle based on a systematic image processing approach toward an automated asphalt pavement inspection,” Automation in Construction, 2016. View at Publisher · View at Google Scholar
  • Yashon O. Ouma, and Michael Hahn, “Wavelet-morphology based detection of incipient linear cracks in asphalt pavements from RGB camera imagery and classification using circular Radon transform,” Advanced Engineering Informatics, vol. 30, no. 3, pp. 481–499, 2016. View at Publisher · View at Google Scholar
  • P. Bibiloni, M. González-Hidalgo, and S. Massanet, “A Survey on Curvilinear Object Segmentation in Multiple Applications,” Pattern Recognition, 2016. View at Publisher · View at Google Scholar
  • Hongcheng Wang, Ziyou Xiong, Alan M. Finn, and Zaffir Chaudhry, “A context-driven approach to image-based crack detection,” Machine Vision and Applications, 2016. View at Publisher · View at Google Scholar
  • V. Baltazart, J.-M. Moliard, R. Amhaz, L.-M. Cottineau, A. Wright, D. Wright, and M. Jethwa, “Automatic Crack Detection on Pavement Images for Monitoring Road Surface Conditions—Some Results from the Collaborative FP7 TRIMM Project,” 8th RILEM International Conference on Mechanisms of Cracking and Debonding in Pavements, vol. 13, pp. 719–724, 2016. View at Publisher · View at Google Scholar
  • Gil Lopes, A. Ribeiro, Neftalí Sillero, Luís Gonçalves-Seco, Cristiano Silva, Marc Franch, and Paulo Trigueiros, “High Resolution Trichromatic Road Surface Scanning with a Line Scan Camera and Light Emitting Diode Lighting for Road-Kill Detection,” Sensors, vol. 16, no. 4, pp. 558, 2016. View at Publisher · View at Google Scholar
  • Qing-Quan Li, Ying Chen, De-Jin Zhang, Min Cao, and Li He, “Asphalt pavement crack detection based on spatial clustering feature,” Zidonghua Xuebao/Acta Automatica Sinica, vol. 42, no. 3, pp. 443–454, 2016. View at Publisher · View at Google Scholar
  • Suwarna Gothane, M. V. Sarode, and K. Srujan Raju, “Design, Construction and Analysis of Model Dataset for Indian Road Network and Performing Classification to Estimate Accuracy of Different Classifier with Its Comparison Summary Evaluation,” Swarm, Evolutionary, and Memetic Computing, vol. 9873, pp. 50–59, 2016. View at Publisher · View at Google Scholar
  • Dejin Zhang, Qingquan Li, Ying Chen, Min Cao, Li He, and Bailing Zhang, “An Efficient and Reliable Coarse-to-fine Approach for Asphalt Pavement Crack Detection,” Image and Vision Computing, 2016. View at Publisher · View at Google Scholar
  • Yong Shi, Limeng Cui, Zhiquan Qi, Fan Meng, and Zhensong Chen, “Automatic Road Crack Detection Using Random Structured Forests,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 12, pp. 3434–3445, 2016. View at Publisher · View at Google Scholar
  • Rabih Amhaz, Sylvie Chambon, Jerome Idier, and Vincent Baltazart, “Automatic Crack Detection on Two-Dimensional Pavement Images: An Algorithm Based on Minimal Path Selection,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 10, pp. 2718–2729, 2016. View at Publisher · View at Google Scholar
  • L. Díaz-Vilariño, H. González-Jorge, M. Bueno, P. Arias, and I. Puente, “Automatic classification of urban pavements using mobile LiDAR data and roughness descriptors,” Construction and Building Materials, vol. 102, pp. 208–215, 2016. View at Publisher · View at Google Scholar
  • Senthan Mathavan, Kanapathippillai Vaheesan, Akash Kumar, Khurram Kamal, Mujib Rahman, Martyn Stonecliffe-Jones, and Chanjief Chandrakumar, “Detection of pavement cracks using tiled fuzzy Hough transform,” Journal of Electronic Imaging, vol. 26, no. 5, 2017. View at Publisher · View at Google Scholar
  • Xianfeng Zhang, Qingxi Tong, Min Sun, Yifan Pan, and Lun Luo, “Progress on road pavement condition detection based on remote sensing monitoring,” Yaogan Xuebao/Journal of Remote Sensing, vol. 21, no. 5, pp. 796–811, 2017. View at Publisher · View at Google Scholar
  • Marcin Staniek, “Road Infrastructure Condition Assessment as Element of Road Traffic Safety – Concept of the RCT Solution in the S-mileSys Platform,” Recent Advances in Traffic Engineering for Transport Networks and Systems, vol. 21, pp. 65–72, 2017. View at Publisher · View at Google Scholar
  • P. Bibiloni, M. González-Hidalgo, and S. Massanet, “General-Purpose Curvilinear Object Detection with Fuzzy Mathematical Morphology,” Applied Soft Computing, 2017. View at Publisher · View at Google Scholar
  • Wei Li, Ju Huyan, Susan L. Tighe, Qing-qing Ren, and Zhao-yun Sun, “Three-Dimensional Pavement Crack Detection Algorithm Based on Two-Dimensional Empirical Mode Decomposition,” Journal of Transportation Engineering, Part B: Pavements, vol. 143, no. 2, pp. 04017005, 2017. View at Publisher · View at Google Scholar
  • Anan Banharnsakun, “Hybrid ABC-ANN for pavement surface distress detection and classification,” International Journal of Machine Learning and Cybernetics, vol. 8, no. 2, pp. 699–710, 2017. View at Publisher · View at Google Scholar
  • Gang Li, Xiaoxing Zhao, Kai Du, Feng Ru, and Yubo Zhang, “Recognition and evaluation of bridge cracks with modified active contour model and greedy search-based support vector machine,” Automation in Construction, 2017. View at Publisher · View at Google Scholar
  • Kasthurirangan Gopalakrishnan, Siddhartha K. Khaitan, Alok Choudhary, and Ankit Agrawal, “Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection,” Construction and Building Materials, vol. 157, pp. 322–330, 2017. View at Publisher · View at Google Scholar
  • Yashon O. Ouma, and M. Hahn, “Pothole detection on asphalt pavements from 2D-colour pothole images using fuzzy c -means clustering and morphological reconstruction,” Automation in Construction, vol. 83, pp. 196–211, 2017. View at Publisher · View at Google Scholar
  • Xianfeng Zhang, Jie Tian, Yifan Pan, Xu Jin, Lun Luo, and Ke Yang, “Mapping asphalt pavement aging and condition using multiple endmember spectral mixture analysis in Beijing, China,” Journal of Applied Remote Sensing, vol. 11, no. 1, 2017. View at Publisher · View at Google Scholar
  • Francesco Marinello, Giuseppe Zimbalatti, Andrea Pezzuolo, Raffaele Cavalli, Andrea Rosario Proto, and Stefano Grigolato, “Determination of forest road surface roughness by kinect depth imaging,” Annals of Forest Research, vol. 60, no. 2, pp. 217–226, 2017. View at Publisher · View at Google Scholar
  • Yusuke Fujita, Koji Shimada, Manabu Ichihara, and Yoshihiko Hamamoto, “A method based on machine learning using hand-crafted features for crack detection from asphalt pavement surface images,” Proceedings of SPIE - The International Society for Optical Engineering, vol. 10338, 2017. View at Publisher · View at Google Scholar
  • Akiyoshi Hizukuri, and Takeshi Nagata, “Development of a classification method for a crack on a pavement surface images using machine learning,” Proceedings of SPIE - The International Society for Optical Engineering, vol. 10338, 2017. View at Publisher · View at Google Scholar
  • Dean Wright, “Pseudo feature point registration of pavement images,” Journal of Traffic and Transportation Engineering (English Edition), vol. 5, no. 4, pp. 254–267, 2018. View at Publisher · View at Google Scholar
  • Nhat-Duc Hoang, and Quoc-Lam Nguyen, “Automatic Recognition of Asphalt Pavement Cracks Based on Image Processing and Machine Learning Approaches: A Comparative Study on Classifier Performance,” Mathematical Problems in Engineering, vol. 2018, pp. 1–16, 2018. View at Publisher · View at Google Scholar
  • Zhong Qu, Fang-Rong Ju, Yang Guo, Ling Bai, and Kuo Chen, “Concrete surface crack detection with the improved pre-extraction and the second percolation processing methods,” Plos One, vol. 13, no. 7, pp. e0201109, 2018. View at Publisher · View at Google Scholar
  • Kasthurirangan Gopalakrishnan, “Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review,” Data, vol. 3, no. 3, pp. 28, 2018. View at Publisher · View at Google Scholar
  • Emir Buza, Amila Akagic, Samir Omanovic, and Almir Karabegovic, “Pavement crack detection using Otsu thresholding for image segmentation,” 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2018 - Proceedings, pp. 1092–1097, 2018. View at Publisher · View at Google Scholar
  • Wissam Kaddah, Marwa Elbouz, Yousri Ouerhani, Vincent Baltazart, Marc Desthieux, and Ayman Alfalou, “Optimized minimal path selection (OMPS) method for automatic and unsupervised crack segmentation within two-dimensional pavement images,” The Visual Computer, 2018. View at Publisher · View at Google Scholar
  • Turki I. Al-Suleiman, Zoubir M. Hamici, Subhi M. Bazlamit, and Hesham S. Ahmad, “Assessment of the Effect of Alligator Cracking on Pavement Condition Using WSN-Image Processing,” 8th International Conference on Engineering, Project, and Product Management (EPPM 2017), pp. 265–274, 2018. View at Publisher · View at Google Scholar
  • Mahsa Payab, Reza Abbasina, and Mostafa Khanzadi, “A Brief Review and a New Graph-Based Image Analysis for Concrete Crack Quantification,” Archives of Computational Methods in Engineering, vol. 26, no. 2, pp. 347–365, 2018. View at Publisher · View at Google Scholar
  • Laura Inzerillo, Gaetano Di Mino, and Ronald Roberts, “Image-based 3D reconstruction using traditional and UAV datasets for analysis of road pavement distress,” Automation in Construction, vol. 96, pp. 457–469, 2018. View at Publisher · View at Google Scholar
  • Daniel Seichter, Ronny Stricker, Markus Eisenbach, and Horst-Michael Gross, “How to Improve Deep Learning based Pavement Distress Detection while Minimizing Human Effort,” IEEE International Conference on Automation Science and Engineering, vol. 2018-, pp. 63–70, 2018. View at Publisher · View at Google Scholar
  • Antonella Ragnoli, Maria De Blasiis, and Alessandro Di Benedetto, “Pavement Distress Detection Methods: A Review,” Infrastructures, vol. 3, no. 4, pp. 58, 2018. View at Publisher · View at Google Scholar
  • Thomas Arthur Carr, Maria Insa Iglesias, Tom Buggy, Mark David Jenkins, and Gordon Morison, “A deep convolutional neural network for semantic pixel-wise segmentation of road and pavement surface cracks,” European Signal Processing Conference, vol. 2018-, pp. 2120–2124, 2018. View at Publisher · View at Google Scholar
  • David Fernandes, Paulo Lobato Correia, and Henrique Oliveira, “Road surface crack detection using a light field camera,” European Signal Processing Conference, vol. 2018-, pp. 2135–2139, 2018. View at Publisher · View at Google Scholar
  • Yifan Pan, Guido Cervone, Xianfeng Zhang, and Liping Yang, “Detection of Asphalt Pavement Potholes and Cracks Based on the Unmanned Aerial Vehicle Multispectral Imagery,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 10, pp. 3701–3712, 2018. View at Publisher · View at Google Scholar
  • Luis Daniel Otero, Mark Moyou, Adrian Peter, and Carlos E. Otero, “Towards a Remote Sensing System for Railroad Bridge Inspections: A Concrete Crack Detection Component,” Conference Proceedings - IEEE SOUTHEASTCON, vol. 2018-, 2018. View at Publisher · View at Google Scholar
  • Peter Cheng-Yang Liu, and Nora El-Gohary, “Automatic Annotation of Web Images for Domain-Specific Crack Classification,” Advances in Informatics and Computing in Civil and Construction Engineering, pp. 553–560, 2018. View at Publisher · View at Google Scholar
  • T.H. Nguyen, T.L. Nguyen, D.N. Sidorov, and A.I. Dreglea, “Machine learning algorithms application to road defects classification,” Intelligent Decision Technologies, pp. 1–8, 2018. View at Publisher · View at Google Scholar
  • Kui Li, Xiaomin Xie, Tingting Wang, Bo Liu, and Lin Zhang, “Crack detection for concrete architecture images using feature enhancement filtering and shape guided active contour model,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11259, pp. 16–26, 2018. View at Publisher · View at Google Scholar
  • Xiaomin Xie, Yilin Xia, Bo Liu, Kui Li, and Tingting Wang, “The multichannel integration active contour framework for crack detection,” International Journal of Advanced Robotic Systems, vol. 16, no. 3, pp. 172988141985285, 2019. View at Publisher · View at Google Scholar
  • Xiao-Wei Ye, Tao Jin, and Peng-Yu Chen, “Structural crack detection using deep learning–based fully convolutional networks,” Advances in Structural Engineering, pp. 136943321983629, 2019. View at Publisher · View at Google Scholar
  • Nirmal Dhakal, Mohammad Bashar, and Mostafa Elseifi, “Guidelines for Identification of Top-down Cracks (TDC) in In-Service Flexible Pavements,” MATEC Web of Conferences, vol. 271, pp. 08004, 2019. View at Publisher · View at Google Scholar
  • M J Chin, P Babashamsi, and N I M Yusoff, “A comparative study of monitoring methods in sustainable pavement management system,” IOP Conference Series: Materials Science and Engineering, vol. 512, pp. 012039, 2019. View at Publisher · View at Google Scholar
  • Lei Zhang, Weichi Xu, Leilei Zhu, Xiaozhe Yuan, and Chuang Zhang, “Study on Pavement Defect Detection Based on Image Processing Utilizing UAV,” Journal of Physics: Conference Series, vol. 1168, pp. 042011, 2019. View at Publisher · View at Google Scholar
  • Sylvie Le Hégarat-Mascle, Emanuel Aldea, and Jennifer Vandoni, “Efficient evaluation of the Number of False Alarm criterion,” EURASIP Journal on Image and Video Processing, vol. 2019, no. 1, 2019. View at Publisher · View at Google Scholar
  • Michal Ferenčík, Miroslav Kardoš, Michal Allman, and Zuzana Slatkovská, “Detection of forest road damage using mobile laser profilometry,” Computers and Electronics in Agriculture, vol. 166, pp. 105010, 2019. View at Publisher · View at Google Scholar
  • Yahui Liu, Jian Yao, Xiaohu Lu, Renping Xie, and Li Li, “DeepCrack: A Deep Hierarchical Feature Learning Architecture for Crack Segmentation,” Neurocomputing, 2019. View at Publisher · View at Google Scholar
  • Hwee Kwon Jung, and Gyuhae Park, “Rapid and non-invasive surface crack detection for pressed-panel products based on online image processing,” Structural Health Monitoring, pp. 147592171881115, 2019. View at Publisher · View at Google Scholar
  • Siyuan Wu, Jie Fang, Xiangtao Zheng, and Xijie Li, “Sample and Structure-Guided Network for Road Crack Detection,” IEEE Access, vol. 7, pp. 130032–130043, 2019. View at Publisher · View at Google Scholar
  • Sarode, Suwarna Gothane, and Thakre, “Prediction for indian road network images dataset using feature extraction method,” Advances in Intelligent Systems and Computing, vol. 815, pp. 125–138, 2019. View at Publisher · View at Google Scholar