EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 290 (2021) E3S Web Conf., 290 (2021) 02020 References
Open Access
Issue
E3S Web Conf.
Volume 290, 2021
2021 3rd International Conference on Geoscience and Environmental Chemistry (ICGEC 2021)
Article Number 02020
Number of page(s) 9
Section Geological and Hydrological Structure and Environmental Planning
DOI https://doi.org/10.1051/e3sconf/202129002020
Published online 14 July 2021
  1. X. Wang, A. Wang, T. Jiang, Y. Yang, and B. Zhang. (2019) Review of application areas for side scan sonar image. Bulletin of Surveying and Mapping, 01: 1-4. [Google Scholar]
  2. S. Reed, Y. Petillot, and J. Bell. (2003) An automatic approach to the detection and extraction of mine features in sidescan sonar. IEEE Journal of Oceanic Engineering, 28: 90–105. [Google Scholar]
  3. F. Yang, Z. DU, Z. Wu, J. Li, and F. Chu. (2006) Object Recognizing on Sonar Image Based on Histogram and Geometric Feature. Marine Science Bulletin, 05: 64–69. [Google Scholar]
  4. S. Reed, Y. Petillot, and J. Bell. (2004) Model-based approach to the detection and classification of mines in sidescan sonar. Applied Optics, 43: 237–246. [PubMed] [Google Scholar]
  5. H. Guo, T. Tian, G. Liu, and F. Lu. (2002) A Recognizing Method Based on Fuzzy Clustering on the Sonar Image of a Small Target on the Sea Bed. Journal of Chinese Computer Systems, 02: 139–141. [Google Scholar]
  6. G. J. Dobeck, J. C. Hyland, and L. Smedley. (1997) Automated detection and classification of sea mines in sonar imagery. in: Spie -Int Soc Optical Engineering. Bellingham. pp. 90–110. [Google Scholar]
  7. L. Zheng and K. Tian. (2018) Detection of small objects in sidescan sonar images based on POHMT and Tsallis entropy. Signal Processing, 142: 168–177. [Google Scholar]
  8. E. Dura, Z. Yan, L. Xuejun, G. J. Dobeck, and L. Carin. (2005) Active learning for detection of mine-like objects in side-scan sonar imagery. IEEE Journal of Oceanic Engineering, 30: 360–371. [Google Scholar]
  9. F. Liu, S. Wang, and Y. Zhang. (2018) Overview on Models and Applications of Support Vector Machine. Computer Systems & Applications, 27: 1–9. [Google Scholar]
  10. A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi.(2020) A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review, 53: 5455–5516. [Google Scholar]
  11. S. Peng, W. Jiang, H. Pi, X. Li, H. Bao, and X. Zhou. (2020) Deep Snake for Real-Time Instance Segmentation. in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 8530–8539. [Google Scholar]
  12. M. Ding. (2020) Learning Depth-Guided Convolutions for Monocular 3D Object Detection. in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 11669–11678. [Google Scholar]
  13. E. Shelhamer, J. Long, and T. Darrell. (2017) Fully Convolutional Networks for Semantic Segmentation. Ieee Transactions on Pattern Analysis And Machine Intelligence, 39: 640–651. [PubMed] [Google Scholar]
  14. Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. (2014) DeepFace: Closing the Gap to Human-Level Performance in Face Verification. in 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus. pp. 1701–1708. [Google Scholar]
  15. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. (1998) Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86: 2278–2324. [CrossRef] [Google Scholar]
  16. A. Krizhevsky, I. Sutskever, and G. E. Hinton. (2017) ImageNet Classification with Deep Convolutional Neural Networks. Communications Of the Acm, 60: 84–90. [Google Scholar]
  17. C. Szegedy. Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston. pp. 1–9. [Google Scholar]
  18. K. He, X. Zhang, S. Ren, and J. Sun. (2016) Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas. pp. 770–778. [Google Scholar]
  19. V. Badrinarayanan, A. Kendall, and R. Cipolla. (2017) SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. Ieee Transactions on Pattern Analysis And Machine Intelligence, 39: 2481–2495. [PubMed] [Google Scholar]
  20. K. Simonyan and A. Zisserman. (2015) Very Deep Convolutional Networks for Large-Scale Image Recognition. Computer Science. [Google Scholar]
  21. Z. Shao, W. Wu, Z. Wang, W. Du, and C. Li. (2018) SeaShips: A Large-Scale Precisely Annotated Dataset for Ship Detection. IEEE Transactions on Multimedia, 20: 2593–2604. [Google Scholar]
  22. M. Valdenegro-Toro. (2017) Best practices in convolutional networks for forward-looking sonar image recognition. in: OCEANS 2017. Aberdeen. pp. 1–9. [Google Scholar]
  23. H. Liang, L. Jin, and C. Yang. (2019) Research on Underwater Target Recognition Based on Depth Learning with Small Sample. journal of Wuhan University of Technology (Transportation Science & Engineering), 43: 6–10. [Google Scholar]
  24. R. Mehrotra, M. A. Ansari, R. Agrawal, and R. S. Anand. (2020) A Transfer Learning approach for AI-based classification of brain tumors. Machine Learning with Applications, 2. [Google Scholar]
  25. X. Zhen, B. Pan, and J. Zhang. (2020) Power tower detection in remote sensing imagery based on deformable network and transfer learning. Acta Geodaetica et Cartographica Sinica, 49: 1042–1050. [Google Scholar]
  26. Z. Zhu, X. Fu, and Y. Hu. (2020) A sonar Image Recognition Method Based on Convolutional Neural Network Trained through Transfer Learning,” Journal of Unmanned Undersea Systems, 28: 89–96. [Google Scholar]
  27. S. J. Pan and Q. A. Yang.(2010) A Survey on Transfer Learning. Ieee Transactions on Knowledge And Data Engineering, 22: 1345–1359. [Google Scholar]
  28. J. Yosinski, J. Clune, Y. Bengio, and H. Lipson. (2014) How transferable are features in deep neural networks?. Advances In Neural Information Processing Systems, vol. 27. [Google Scholar]
  29. A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson. (2014) CNN Features Off-the-Shelf: An Astounding Baseline for Recognition. in: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Columbus. pp. 512–519. [Google Scholar]
  30. J. Howard and S. Ruder. (2018) Universal Language Model Fine-tuning for Text Classification. in: Proceedings Of the 56th Annual Meeting Of the Association for Computational Linguistics. Melbourne. pp. 328–339. [Google Scholar]
  31. Z. Wang, X. XU, and L. Zhang. (2020) Study of deep transfer learning for SAR ATR based on simulated SAR images. ournal of University of Chinese Academy of Sciences, 37: 516–524. [Google Scholar]
  32. J. Liu, N. Liu, Z. Pan, and X. You. (2019) Domain adaptive methods for device diversity in indoor localization. Journal of Southeast University(English Edition), 35: 424–430. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.