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All issues Volume 144 (2022) SHS Web Conf., 144 (2022) 03015 References
Open Access
Issue
SHS Web Conf.
Volume 144, 2022
2022 International Conference on Science and Technology Ethics and Human Future (STEHF 2022)
Article Number 03015
Number of page(s) 9
Section Application of Artificial Intelligence Technology and Machine Learning Algorithms
DOI https://doi.org/10.1051/shsconf/202214403015
Published online 26 August 2022
  1. Nama, Pooja, et al.. Study on causes of cracks & its preventive measures in concrete structures [J]. International Journal of Engineering Research and Applications, 2015, 5(5): 119-123. [Google Scholar]
  2. Yang X, Li H, Yu Y, et al.. Automatic pixel‐level crack detection and measurement using fully convolutional network[J]. Computer‐Aided Civil and Infrastructure Engineering, 2018, 33(12): 1090-1109. [CrossRef] [Google Scholar]
  3. Gavilán M, Balcones D, Marcos O, et al.. Adaptive road crack detection system by pavement classification[J]. Sensors, 2011, 11(10): 9628-9657. [CrossRef] [Google Scholar]
  4. Fujita Y, Hamamoto Y. A robust automatic crack detection method from noisy concrete surfaces[J]. Machine Vision and Applications, 2011, 22(2): 245254. [CrossRef] [Google Scholar]
  5. Zou Q, Cao Y, Li Q, et al.. CrackTree: Automatic crack detection from pavement images[J]. Pattern Recognition Letters, 2012, 33(3): 227-238. [CrossRef] [Google Scholar]
  6. Prasanna P, Dana K J, Gucunski N, et al.. Automated crack detection on concrete bridges[J]. IEEE Transactions on automation science and engineering, 2014, 13(2): 591-599. [Google Scholar]
  7. Guo Y, Liu Y, Oerlemans A, et al.. Deep learning for visual understanding: A review[J]. Neurocomputing, 2016, 187: 27-48. [CrossRef] [Google Scholar]
  8. Zhang L, Yang F, Zhang Y D, et al.. Road crack detection using deep convolutional neural network[C]//2016 IEEE international conference on image processing (ICIP). IEEE, 2016: 3708-3712. [Google Scholar]
  9. Zhang A, Wang K C P, Li B, et al.. Automated pixel‐ level pavement crack detection on 3D asphalt surfaces using a deep‐learning network[J]. Computer‐Aided Civil and Infrastructure Engineering, 2017, 32(10): 805-819. [CrossRef] [Google Scholar]
  10. Sun M, Song Z, Jiang X, et al.. Learning pooling for convolutional neural network[J]. Neurocomputing, 2017, 224: 96-104. [CrossRef] [Google Scholar]
  11. Redmon J, Divvala S, Girshick R, et al.. You only look once: Unified, real-time object detection [C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779-788. [Google Scholar]
  12. Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 7263-7271. [Google Scholar]
  13. Redmon J, Farhadi A. Yolov3: An incremental improvement [J]. arXiv preprint arXiv:1804.02767, 2018. [Google Scholar]
  14. Bochkovskiy A, Wang C Y, Liao H Y M. Yolov4: Optimal speed and accuracy of object detection[J]. arXiv preprint arXiv:2004.10934, 2020. [Google Scholar]
  15. 2018 – Özgenel, Ç.F., Gönenç Sorguç, A. “Performance Comparison of Pretrained Convolutional Neural Networks on Crack Detection in Buildings”, ISARC 2018, Berlin. [Google Scholar]
  16. Tian Z, Shen C, Chen H, et al.. Fcos: Fully convolutional one-stage object detection [C]// Proceedings of the IEEE/CVF international conference on computer vision. 2019: 9627-9636. [Google Scholar]
  17. Rezatofighi H, Tsoi N, Gwak J Y, et al.. Generalized intersection over union: A metric and a loss for bounding box regression[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 658-666. [Google Scholar]
  18. Wang C Y, Liao H Y M, Wu Y H, et al.. CSPNet: A new backbone that can enhance learning capability of CNN[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. 2020: 390-391. [Google Scholar]
  19. Lin T Y, Dollár P, Girshick R, et al.. Feature pyramid networks for object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 2117-2125. [Google Scholar]
  20. Liu S, Qi L, Qin H, et al.. Path aggregation network for instance segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 8759-8768. [Google Scholar]
  21. Jiao S, Li X, Lu X. An improved Ostu method for image segmentation[C]//2006 8th international Conference on Signal Processing. IEEE, 2006, 2. [Google Scholar]
  22. Likas A, Vlassis N, Verbeek J J. The global k-means clustering algorithm[J]. Pattern recognition, 2003, 36(2): 451-461. [CrossRef] [Google Scholar]

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