Open Access
Issue
ITM Web Conf.
Volume 59, 2024
II International Workshop “Hybrid Methods of Modeling and Optimization in Complex Systems” (HMMOCS-II 2023)
Article Number 02013
Number of page(s) 8
Section Interdisciplinary Mathematical Modeling and Applications
DOI https://doi.org/10.1051/itmconf/20245902013
Published online 25 January 2024
  1. R. Gupta, K. D. M. Rao, et al., ACS Applied Materials & Interfaces, 6(16), 1368813696 (2014) [Google Scholar]
  2. A.S. Voronin, Y.V. Fadeev, I.V. Govorun, et al., J Mater Sci 56, 14741–14762 (2021). https://doi.org/10.1007/s10853-021-06206-4 [CrossRef] [Google Scholar]
  3. A.S. Voronin, et al., Materials 15, 1449 (2022) https://doi.org/10.3390/ma15041449 [CrossRef] [Google Scholar]
  4. D. Li, et al., Journal of Engineering Science and Technology Review 12 148–156 (2019) [CrossRef] [Google Scholar]
  5. Y. Chen, Z. Zhu, Z. Lin, Y. Zhou, Buildings 13, 1814 (2023) [CrossRef] [Google Scholar]
  6. Q. Q. Li, X. L. Liu, Novel approach to pavement image segmentation based on neighboring difference histogram method In: IEEE 2008 Congress on Image and Signal Processing (Sanya, China: IEEE, 2008) pp. 792–796. [Google Scholar]
  7. S. J. Schmugge, et al., Crack segmentation by leveraging multiple frames of varying illumination In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), (California, USA: IEEE, 2017), pp. 1045–1053. [CrossRef] [Google Scholar]
  8. H. N. Nguyen, T. Y. Kam, P. Y. Cheng, Journal of Signal Processing System, 77(3), 221–240 (2014) [CrossRef] [Google Scholar]
  9. L. Zhang, F. Yang, Y. D. Zhang, et al., Road crack detection using deep convolutional neural network In: 2016 IEEE International Conference on Image Processing (ICIP) (Arizona, USA: IEEE, 2016), pp. 3708–3712 [Google Scholar]
  10. J. Valença, D. Dias-Da-Costa, E. Júlio, et al., Measurement, 46(1), 433–441 (2013) [CrossRef] [Google Scholar]
  11. Y. Hu, C. Zhao, Pattern Recognition Research, 1(20103), 140–147 (2010) [CrossRef] [MathSciNet] [Google Scholar]
  12. K. McCormack, et al., Journal of Computing in Civil Engineering, 30(2), 1–11 (2016) [Google Scholar]
  13. S. Katsigiannis, et al., J. Build. Eng. 76, 107105 (2023) [CrossRef] [Google Scholar]
  14. L.-C. Chen, et al., Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation In: Proceedings of the 2018 European Conference on Computer Vision (ECCV), (Munich, Germany, 8–14 September 2018) pp. 801–818. [Google Scholar]
  15. B. Dwyer, et. al. Roboflow (Version 1.0) [Software]. URL: https://roboflow.com.computervision. [Google Scholar]
  16. G. Bradski, A. Kaehler, 2008. Learning OpenCV: Computer vision with the OpenCV library, " O'Reilly Media, Inc." [Google Scholar]
  17. G. Jocher, A. Chaurasia, J. Qiu, 2023. YOLO by Ultralytics (Version 8.0.0) [Computer software]. URL: https://github.com/ultralytics/ultralytics [Google Scholar]
  18. L. Shapiro, G. Stockman, Computer vision (Prentice Hall, 2001) [Google Scholar]
  19. S. Suzuki et al., Computer Vision, Graphics, and Image Processing, 30(1) 32–46 (1985) [CrossRef] [Google Scholar]
  20. D. M W Powers, Journal of Machine Learning Technologies 2(1) 37–63 (2011) [Google Scholar]

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