• DE
  • EN
  • FR
  • Base de données et galerie internationale d'ouvrages d'art et du génie civil

Publicité

A Novel Approach for Automatic Detection of Concrete Surface Voids Using Image Texture Analysis and History-Based Adaptive Differential Evolution Optimized Support Vector Machine

  1. Ozkul Tarik (2011), "Design and optimization of an instrument for measuring bughole rating of concrete surfaces" in Journal of the Franklin Institute, v. 348, n. 7, Elsevier BV, p. 1377-1392

    https://doi.org/10.1016/j.jfranklin.2010.04.004

  2. Benito Saorin Francisco (2018), "QSI Methods for Determining the Quality of the Surface Finish of Concrete" in Sustainability, v. 10, n. 4, MDPI AG, p. 931

    https://doi.org/10.3390/su10040931

  3. Gao Mingxing (2020), "Detection and Segmentation of Cement Concrete Pavement Pothole Based on Image Processing Technology" in Mathematical Problems in Engineering, v. 2020, Hindawi Limited, p. 1-13

    https://doi.org/10.1155/2020/1360832

  4. Hoang Nhat-Duc (2018), "Image Processing-Based Recognition of Wall Defects Using Machine Learning Approaches and Steerable Filters" in Computational Intelligence and Neuroscience, v. 2018, Hindawi Limited, p. 1-18

    https://doi.org/10.1155/2018/7913952

  5. Kim Hyunjun (2019), "Crack and Noncrack Classification from Concrete Surface Images Using Machine Learning" in Structural Health Monitoring, v. 18, n. 3, SAGE Publications, p. 725-738

    https://doi.org/10.1177/1475921718768747

  6. Qingguo Tian (2019), "A methodology framework for retrieval of concrete surface crack′s image properties based on hybrid model" in Optik, v. 180, Elsevier BV, p. 199-214

    https://doi.org/10.1016/j.ijleo.2018.11.013

  7. Perez Husein (2019), "Deep Learning for Detecting Building Defects Using Convolutional Neural Networks" in Sensors, v. 19, n. 16, MDPI AG, p. 3556

    https://doi.org/10.3390/s19163556

  8. Song Weidong (2020), "Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features" in Journal of Advanced Transportation, v. 2020, Hindawi Limited, p. 1-11

    https://doi.org/10.1155/2020/6412562

  9. "Comparison of Methods for Texture Assessment of Concrete Surfaces" in ACI Materials Journal, v. 107, n. 5, American Concrete Institute

    https://doi.org/10.14359/51663962

  10. Fekri-Ershad Shervan (2012), "A robust approach for surface defect detection based on one dimensional local binary patterns" in Indian Journal of Science and Technology, v. 5, n. 8, Indian Society for Education and Environment, p. 1-7

    https://doi.org/10.17485/ijst/2012/v5i8.12

  11. da Silva Wilson Ricardo Leal (2013), "Expert system applied for classifying self-compacting concrete surface finish" in Advances in Engineering Software, v. 64, Elsevier BV, p. 47-61

    https://doi.org/10.1016/j.advengsoft.2013.04.005

  12. Tajeripour Farshad (2014), "Developing a Novel Approach for Stone Porosity Computing Using Modified Local Binary Patterns and Single Scale Retinex" in Arabian Journal for Science and Engineering, v. 39, n. 2, Springer Science and Business Media LLC, p. 875-889

    https://doi.org/10.1007/s13369-013-0725-8

  13. Goïc Gaëtan Le (2016), "Multiscale roughness analysis of engineering surfaces: A comparison of methods for the investigation of functional correlations" in Mechanical Systems and Signal Processing, v. 66-67, Elsevier BV, p. 437-457

    https://doi.org/10.1016/j.ymssp.2015.05.029

  14. Dash Sonali (2018), "Gray level run length matrix based on various illumination normalization techniques for texture classification" in Evolutionary Intelligence, Springer Science and Business Media LLC

    https://doi.org/10.1007/s12065-018-0164-2

  15. Yoshitake Isamu (2018), "Image analysis for the detection and quantification of concrete bugholes in a tunnel lining" in Case Studies in Construction Materials, v. 8, Elsevier BV, p. 116-130

    https://doi.org/10.1016/j.cscm.2018.01.002

  16. Dunn D. (1995), "Optimal Gabor filters for texture segmentation" in IEEE Transactions on Image Processing, v. 4, n. 7, Institute of Electrical and Electronics Engineers (IEEE), p. 947-964

    https://doi.org/10.1109/83.392336

  17. Kim Nam Chul (2018), "Directional statistical Gabor features for texture classification" in Pattern Recognition Letters, v. 112, Elsevier BV, p. 18-26

    https://doi.org/10.1016/j.patrec.2018.05.010

  18. Medina Roberto (2011), "Automated visual classification of frequent defects in flat steel coils" in The International Journal of Advanced Manufacturing Technology, v. 57, n. 9-12, Springer Science and Business Media LLC, p. 1087-1097

    https://doi.org/10.1007/s00170-011-3352-0

  19. Sun Z. (2005), "On-Road Vehicle Detection Using Evolutionary Gabor Filter Optimization" in IEEE Transactions on Intelligent Transportation Systems, v. 6, n. 2, Institute of Electrical and Electronics Engineers (IEEE), p. 125-137

    https://doi.org/10.1109/TITS.2005.848363

  20. Jain Anil K. (1991), "Unsupervised texture segmentation using Gabor filters" in Pattern Recognition, v. 24, n. 12, Elsevier BV, p. 1167-1186

    https://doi.org/10.1016/0031-3203(91)90143-s

  21. Galloway Mary M. (1975), "Texture analysis using gray level run lengths" in Computer Graphics and Image Processing, v. 4, n. 2, Elsevier BV, p. 172-179

    https://doi.org/10.1016/s0146-664x(75)80008-6

  22. Chu A. (1990), "Use of gray value distribution of run lengths for texture analysis" in Pattern Recognition Letters, v. 11, n. 6, Elsevier BV, p. 415-419

    https://doi.org/10.1016/0167-8655(90)90112-f

  23. Hoang Nhat-Duc (2019), "Image Processing-Based Detection of Pipe Corrosion Using Texture Analysis and Metaheuristic-Optimized Machine Learning Approach" in Computational Intelligence and Neuroscience, v. 2019, Hindawi Limited, p. 1-13

    https://doi.org/10.1155/2019/8097213

  24. Hoang Nhat-Duc (2019), "Automatic Detection of Concrete Spalling Using Piecewise Linear Stochastic Gradient Descent Logistic Regression and Image Texture Analysis" in Complexity, v. 2019, Hindawi Limited, p. 1-14

    https://doi.org/10.1155/2019/5910625

  25. Xiaoou Tang (1998), "Texture information in run-length matrices" in IEEE Transactions on Image Processing, v. 7, n. 11, Institute of Electrical and Electronics Engineers (IEEE), p. 1602-1609

    https://doi.org/10.1109/83.725367

  26. Dasarathy Belur V. (1991), "Image characterizations based on joint gray level—run length distributions" in Pattern Recognition Letters, v. 12, n. 8, Elsevier BV, p. 497-502

    https://doi.org/10.1016/0167-8655(91)80014-2

  27. "Differential Evolution" in Natural Computing Series, Springer-Verlag (Berlin/Heidelberg)

    https://doi.org/10.1007/3-540-31306-0

  28. Storn Rainer (1997), in Journal of Global Optimization, v. 11, n. 4, Springer Science and Business Media LLC, p. 341-359

    https://doi.org/10.1023/a:1008202821328

  29. Das Swagatam (2016), "Recent advances in differential evolution – An updated survey" in Swarm and Evolutionary Computation, v. 27, Elsevier BV, p. 1-30

    https://doi.org/10.1016/j.swevo.2016.01.004

  30. Piotrowski Adam P. (2017), "Review of Differential Evolution population size" in Swarm and Evolutionary Computation, v. 32, Elsevier BV, p. 1-24

    https://doi.org/10.1016/j.swevo.2016.05.003

  31. Shen Xin (2018), "A Phase-Based Adaptive Differential Evolution Algorithm for the Economic Load Dispatch Considering Valve-Point Effects and Transmission Losses" in Mathematical Problems in Engineering, v. 2018, Hindawi Limited, p. 1-24

    https://doi.org/10.1155/2018/4585403

  32. Viktorin Adam (2019), "Distance based parameter adaptation for Success-History based Differential Evolution" in Swarm and Evolutionary Computation, v. 50, Elsevier BV, p. 100462

    https://doi.org/10.1016/j.swevo.2018.10.013

  33. Piotrowski Adam P. (2018), "L-SHADE optimization algorithms with population-wide inertia" in Information Sciences, v. 468, Elsevier BV, p. 117-141

    https://doi.org/10.1016/j.ins.2018.08.030

  34. Piotrowski Adam P. (2018), "Step-by-step improvement of JADE and SHADE-based algorithms: Success or failure?" in Swarm and Evolutionary Computation, v. 43, Elsevier BV, p. 88-108

    https://doi.org/10.1016/j.swevo.2018.03.007

  35. Pham Binh Thai (2019), "A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling" in Bulletin of Engineering Geology and the Environment, v. 78, n. 4, Springer Science and Business Media LLC, p. 2865-2886

    https://doi.org/10.1007/s10064-018-1281-y

  36. Wei Hai (2018), "Study on the Magnitude of Reservoir-Triggered Earthquake Based on Support Vector Machines" in Complexity, v. 2018, Hindawi Limited, p. 1-10

    https://doi.org/10.1155/2018/2830690

  37. Joanes D. N. (1998), "Comparing measures of sample skewness and kurtosis" in Journal of the Royal Statistical Society: Series D (The Statistician), v. 47, n. 1, Wiley, p. 183-189

    https://doi.org/10.1111/1467-9884.00122

  38. Tien Bui Dieu (2019), "Spatial prediction of shallow landslide using Bat algorithm optimized machine learning approach: A case study in Lang Son Province, Vietnam" in Advanced Engineering Informatics, v. 42, Elsevier BV, p. 100978

    https://doi.org/10.1016/j.aei.2019.100978

  39. LeCun Yann (2015), "Deep learning" in Nature, v. 521, n. 7553, Springer Science and Business Media LLC, p. 436-444

    https://doi.org/10.1038/nature14539

  40. Qian Ning (1999), "On the momentum term in gradient descent learning algorithms" in Neural Networks, v. 12, n. 1, Elsevier BV, p. 145-151

    https://doi.org/10.1016/s0893-6080(98)00116-6

  41. "Neural Networks: Tricks of the Trade" in Lecture Notes in Computer Science, Springer Berlin Heidelberg (Berlin, Heidelberg)

    https://doi.org/10.1007/978-3-642-35289-8

  42. Skansi Sandro (2018), "Introduction to Deep Learning" in Undergraduate Topics in Computer Science, Springer International Publishing (Cham)

    https://doi.org/10.1007/978-3-319-73004-2

  43. Fekri-Ershad Shervan (2017), "Impulse-Noise Resistant Color-Texture Classification Approach Using Hybrid Color Local Binary Patterns and Kullback–Leibler Divergence" in The Computer Journal, v. 60, n. 11, Oxford University Press (OUP), p. 1633-1648

    https://doi.org/10.1093/comjnl/bxx033

Publicité

  • Informations
    sur cette fiche
  • Reference-ID
    10427987
  • Publié(e) le:
    30.07.2020
  • Modifié(e) le:
    30.07.2020