A NOVEL SEA ICE DETECTION ALGORITHM BASED ON YOLOV10

Jiahao Guo, Daqi Zhu, and Simon X. Yang

References

  1. [1] W. Li, C.Y. Hsu, M. Tedesco, advancing arctic sea ice remotesensing with AI and deep learning: opportunities and challenges,Remote Sensing, 16(20), 2024, 3764.
  2. [2] A. Krizhevsky, I. Sutskever, G.E. Hinton, Rov target graspingstrategy based on visual perception, Advances in NeuralInformation Processing Systems, 25, 2012, 1097–1105.
  3. [3] L. Jiawen, C. Xiang, H. Xueyou, Imagenet classification withdeep convolutional neural networks, International Journal ofRobotics and Automation, 39, 2024, 229–238.
  4. [4] R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich featurehierarchies for accurate object detection and semanticsegmentation, Proc. Proceedings of the IEEE conference oncomputer vision and pattern recognition, Columbus, OH, 2014,580–587.
  5. [5] R. Girshick, Fast R-CNN In: International conference oncomputer vision (ICCV), Proc. International Conference onComputer Vision (lCCV), Santiago, Chile, 2015, 1440–1448.
  6. [6] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov,D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper withconvolutions, Proc. Proceedings of the IEEE conference oncomputer vision and pattern recognition, Boston, MA, 2015,1–9.
  7. [7] K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning forimage recognition, Proc. Proceedings of the IEEE conferenceon computer vision and pattern recognition, LasVegas, NV,2016, 770–778.
  8. [8] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y.Fu, A.C. Berg, SSD: Single shot multibox detector (Cham:Springer International Publishing, 2016), 21–37.
  9. [9] J. Redmon, You only look once: Unified, real-time objectdetection, Proc. Proceedings of the IEEE conference oncomputer vision and pattern recognition, LasVegas, NV, 2016,779–788.
  10. [10] A. Wang, H. Chen, L. Liu, K. Chen, Z. Lin, J. Han, G. Ding,Yolov10: Real-time end-to-end object detection, arXiv preprintarXiv, 2405, 2024, 14458.
  11. [11] T. Liu, J. Feng, D. Fu, Francis, Wenhan. chen, Application of theoptimised YOLOv3 algorithm in substation power equipmentdefect identification and detection , International Journal ofRobotics and Automation, 51, 2023, 235–243.
  12. [12] X. Wei, Application of optimised YOLOv4 algorithm andmulti-source information fusion in blind zone warning anddetection of car A-pillars, International Journal of Roboticsand Automation, 2025, 108–118.
  13. [13] R. Xu, D. Zhu, M. Chen, A novel underwater object detectionenhanced algorithm based on YOLOv5-MH, IET ImageProcessing, 18(12), 2024, 3415–3429.355
  14. [14] X. Zhang, D. Zhu, W. Gan, YOLOv7t-CEBC network forunderwater litter detection, Journal of Marine Science andEngineering, 12(4), 2024, 524.
  15. [15] P. Liu, C. Song, J. Li, S.X. Yang, X. Chen, C. Liu, Q. Fu,Detection of transmission line against external force damagebased on improved YOLOv3, International Journal of Roboticsand Automation, 35(6), 2020.
  16. [16] H. Yuan, L. Tao, Detection and identification of fish inelectronic monitoring data of commercial fishing vessels basedon improved Yolov8, Journal of Dalian Ocean University,38(3), 2023, 533–542.
  17. [17] Q. HU, X. Pan, Y. Yu, R. Liu, et al., Research on realtime detection algorithm for safety helmets in complex workenvironment, Journal of Safety and Environment, 24(05), 2024,1904–1912.
  18. [18] F. Wu, A. Fan, A. Baevski, Y.N. Dauphin, M. Auli, Pay lessattention with lightweight and dynamic convolutions, arXivpreprint arXiv, 1901, 2019, 10430.
  19. [19] J. Mairal, P. Koniusz, Z. Harchaoui, C. Schmid, Convolutionalkernel networks, Advances in Neural Information ProcessingSystems, 27, 2014, 5348–5357.
  20. [20] M. Tan, R. Pang, Q.V. Le, Efficientdet: Scalable andefficient object detection, Proc. Proceedings of the IEEE/CVFconference on computer vision and pattern recognition, Seattle,WA, 2020, 10781–10790.
  21. [21] J. Chen, S. Kao, H. He, W. Zhuo, S. Wen, C.H. Lee, Run, don’twalk: Chasing higher FLOPS for faster neural networks, Proc.Proceedings of the IEEE/CVF conference on computer visionand pattern recognition, Vancouver, BC, 2023, 12021–12031.
  22. [22] Y.N. Dauphin, A. Fan, M. Auli, D. Grangier, Language mod-eling with gated convolutional networks, Proc. Internationalconference on machine learning, Singapore: PMLR, 2017, 933–941.
  23. [23] S. Gehrmann, Y. Deng, A.M. Rush, Bottom-up abstractivesummarization, arXiv preprint arXiv, 1808, 2018, 10792.
  24. [24] Z. Wang, S. Ji, Smoothed dilated convolutions for improveddense prediction, Proc. Proceedings of the 24th ACM SIGKDDInternational Conference on Knowledge Discovery Data Mining,London, UK, 2018, 2486–2495.
  25. [25] S. Liu, L. Qi, H. Qin, J. Shi, J. Jia, Path aggregation networkfor instance segmentation, Proc. Proceedings of the IEEEconference on computer vision and pattern recognition, SaltLake City, UT, 2018, 8759–8768.
  26. [26] T.Y. Lin, P. Doll´ar, R. Girshick, K. He, B. Hariharan, S.Belongie, Feature pyramid networks for object detection, Proc.Proceedings of the IEEE conference on computer vision andpattern recognition, Honolulu, HI, 2017, 2117–2125.
  27. [27] D. Hendrycks, K. Gimpel, Gaussian error linear units (gelus),arXiv preprint arXiv, 1606, 2016, 08415.
  28. [28] V. Nair, G.E. Hinton, Rectified linear units improverestricted boltzmann machines, Proc. Proceedings of the 27thinternational conference on machine learning (ICML-10), Haifa,Israel, 2010, 807-814.

Important Links:

Go Back