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All issues Volume 67 (2024) ITM Web Conf., 67 (2024) 01034 References
Open Access
Issue
ITM Web Conf.
Volume 67, 2024
The 19th IMT-GT International Conference on Mathematics, Statistics and Their Applications (ICMSA 2024)
Article Number 01034
Number of page(s) 10
Section Mathematics, Statistics and Their Applications
DOI https://doi.org/10.1051/itmconf/20246701034
Published online 21 August 2024
  1. K. R. Jones, C. J. Klein, H. S. Grantham, H. P. Possingham, B. S. Halpern, N. D. Burgess, S. H. Butchart, J. G. Robinson, N. Kingston, N. Bhola, One Earth, 2 (2), 188–196, (2020) [CrossRef] [Google Scholar]
  2. M. Doddema, G. Spaargaren, B. Wiryawan, S. R. Bush, Marine Policy, 119, 104100, (2020) [CrossRef] [Google Scholar]
  3. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, Int. J. Comput. Vis., 115, 211–252, (2015) [Google Scholar]
  4. V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, Nature, 518, 529–533, (2015) [NASA ADS] [CrossRef] [Google Scholar]
  5. C.-Y. Wang, A. Bochkovskiy, H.-Y. M. Liao, YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 7464–7475 (2023) [Google Scholar]
  6. M. Tan, Q. Le, Efficientnetv2: Smaller models and faster training, In International conference on machine learning, 10096–10106, (2021) [Google Scholar]
  7. N. E. M. Khalifa, M. H. N. Taha and A. E. Hassanien, Aquarium family fish species identification system using deep neural networks, In International Conference on Advanced Intelligent Systems and Informatics, 347–356, (2019) [Google Scholar]
  8. D. Rathi, S. Jain, S. Indu, Underwater fish species classification using convolutional neural network and deep learning, In 2017 Ninth international conference on advances in pattern recognition (ICAPR), 1–6, (2017) [Google Scholar]
  9. A. Salman, A. Jalal, F. Shafait, A. Mian, M. Shortis, J. Seager, E. Harvey, L&O Methods, 14, 570–585, (2016) [Google Scholar]
  10. A. Saleh, M. Sheaves, D. Jerry and M. R. Azghadi, Expert Syst. Appl., (2022) [Google Scholar]
  11. F. Odone, E. Trucco and A. Verri, Appl. Artif. Intell., 15, 735–745, (2001) [CrossRef] [Google Scholar]
  12. D. Jayasundara, L. Ramanayake, N. Senarath, S. Herath, R. Godaliyadda, P. Ekanayake, V. Herath, S. Ariyawansha, Multispectral Imaging for Automated Fish Quality Grading, In 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS), 321-326, 121841, (2020) [Google Scholar]
  13. M. Mots’oehli, A. Nikolaev, W. B. Igede, J. Lynham, P. J. Mous, P. Sadowski, FishNet: Deep Neural Networks for Low-Cost Fish Stock Estimation, arXiv preprint arXiv:2403.10916, (2024) [Google Scholar]
  14. K. M. Babu, D. Bentall, D. T. Ashton, M. Puklowski, W. Fantham, H. T. Lin, N. P. Tuckey, M. Wellenreuther, L. K. Jesson, J. Roy. Soc. New Zeal., 53 (1), 52–68, (2023) [CrossRef] [Google Scholar]
  15. B. Wu, C. Liu, F. Jiang, J. Li, Z. Yang, Front. Environ. Sci., 11, 1059217, (2023) [CrossRef] [Google Scholar]
  16. H. Yu, Z. Wang, H. Qin and Y. Chen, IEEE Access, (2023) [Google Scholar]
  17. K. S. K. Patro, V. K. Yadav, V. Bharti, A. Sharma, A. Sharma, Natl. Acad. Sci. Lett., 46, 407–412, (2023) [CrossRef] [Google Scholar]
  18. A. Kuswantori, T. Suesut, W. Tangsrirat, G. Schleining, N. Nunak, Appl. Sci., 13 (6), 3812 (2023) [CrossRef] [Google Scholar]
  19. F. Akhyar, L. Novamizanti, T. Putra, E. N. Furqon, M.-C. Chang, C.-Y. Lin, Lightning YOLOv4 for a Surface Defect Detection System for Sawn Lumber, In 2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR) 184–189, (2022) [Google Scholar]
  20. Y. Pratama, Y. Fujimura, T. Funatomi, Y. Mukaigawa, Parasitic Egg Detection and Classification by Utilizing the YOLO Algorithm with Deep Latent Space Image Restoration and GrabCut Augmentation, In 2022 IEEE International Conference on Image Processing (ICIP), 4311–4315, (2022) [CrossRef] [Google Scholar]
  21. J. Terven, D. Cordova-Esparza, Mach. learn. knowl., 5 (4), 1680–1716, (2023) [CrossRef] [Google Scholar]
  22. C. Li, L. Li, H. Jiang, K. Weng, Y. Geng, L. Li, Z. Ke, Q. Li, M. Cheng, W. Nie, YOLOv6: A single-stage object detection framework for industrial applications, arXiv preprint arXiv:2209.02976, (2022) [Google Scholar]
  23. M. Sohan, T. Sai Ram, R. Reddy, C. Venkata, A Review on YOLOv8 and Its Advancements, In International Conference on Data Intelligence and Cognitive Informatics, 529–545, (2024) [Google Scholar]
  24. A. Buslaev, V. I. Iglovikov, E. Khvedchenya, A. Parinov, M. Druzhinin, A. A. Kalinin, Information, 11 (2), 125 (2020) [CrossRef] [Google Scholar]
  25. X. Zhang, X. Zhou, M. Lin, J. Sun, Shufflenet: An extremely efficient convolutional neural network for mobile devices, In Proceedings of the IEEE conference on computer vision and pattern recognition, 6848–6856, (2018) [Google Scholar]
  26. X. Ding, X. Zhang, N. Ma, J. Han, G. Ding, J. Sun, Repvgg: Making vgg-style convnets great again, In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 13733–13742, (2021) [Google Scholar]
  27. K. Han, A. Xiao, E. Wu, J. Guo, C. Xu, Y. Wang, Adv. Neural. Inf. Process. Syst., 34, 15908–15919, (2021) [Google Scholar]
  28. M.-H. Guo, C.-Z. Lu, Z.-N. Liu, M.-M. Cheng, S.-M. Hu, Comput. Vis. Media., 9 (4), 733–752, (2022) [Google Scholar]
  29. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.-C. Chen, Mobilenetv2: Inverted residuals and linear bottlenecks, In Proceedings of the IEEE conference on computer vision and pattern recognition, 4510–4520, (2018) [Google Scholar]
  30. B. Zoph, V. Vasudevan, J. Shlens, Q. V. Le, Learning transferable architectures for scalable image recognition, In Proceedings of the IEEE conference on computer vision and pattern recognition, 8697–8710, (2018) [Google Scholar]
  31. O. Ulucan, D. Karakaya, M. Turkan, A large-scale dataset for fish segmentation and classification, In 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), 1–5, (2020) [Google Scholar]
  32. M. Everingham, L. Van Gool, C. K. Williams, J. Winn, A. Zisserman, Int. J. Comput. Vis., 88, 303–308, (2010) [CrossRef] [Google Scholar]

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