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A CNN-Based Semi-supervised Self-training Method for Robust Underwater Fish Recognition

Published: 17 April 2024 Publication History

Abstract

Recent AI advances have revolutionized automation in diverse fields. However, despite object detection research progress, underwater fish species identification remains underexplored. Underwater fish recognition is challenged by the unique underwater environment, fish diversity, and limited labeled data. This study introduces a semi-supervised self-training method, using YOLOv5 as the foundation. Our approach iteratively refines the model with labeled and unlabeled data, enhancing accuracy in data-scarce scenarios. Random data augmentation is also adopted to bolsters model robustness, addressing the complexities of underwater environments.

References

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Chiranjibi Shah, Simegnew Yihunie Alaba, M. M. Nabi, Jack Prior, Matthew Campbell, Farron Wallace, John E. Ball, and Robert Moorhead, "An enhanced YOLOv5 model for fish species recognition from underwater environments", Proc. SPIE 12543, Ocean Sensing and Monitoring XV, 125430O, 12 June 2023; https://doi.org/10.1117/12.2663408
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Boaz Zion, “The use of computer vision technologies in aquaculture – A review”. Computers and Electronics in Agriculture,01 Oct 2012, 88:125-132.
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Liu Q, Gong X, Li J, Wang H, Liu R, Liu D, Zhou R, Xie T, Fu R, Duan X. 2023. “A multitask model for realtime fish detection and segmentation based on YOLOv5”. PeerJ Computer Science 9:e1262
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S.K. Aruna, N. Deepa, T Devi, “Underwater Fish Identification in Real-Time using Convolutional Neural Network”. 2023 7th International Conference on Intelligent Computing and Control Systems.
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Shasha Li; Yongjun Li; Yao Li; Mengjun Li; Xiaorong Xu, “YOLO-FIRI: Improved YOLOv5 for Infrared Image Object Detection”. 2021.
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Qisong Song, Shaobo Li, Qiang Bai, Jing Yang, Xingxing Zhang, Zhiang Li, Zhongjing Duan, “Object Detection Method for Grasping Robot Based on Improved YOLOv5”; Micromachines 2021, 12(11), 1273; https://doi.org/10.3390/mi12111273
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WEN H M, TONG M J. Object detection in automatic driving scenarios based on Semi-supervised learning[J]. Microelectronics & Comput, 2023, 40 (2): 22-36.
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Hyun-Ki Jung, Gi-Sang Choi, “Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions”. Appl. Sci. 2022, 12(14), 7255; https://doi.org/10.3390/app12147255

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  1. A CNN-Based Semi-supervised Self-training Method for Robust Underwater Fish Recognition

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    EITCE '23: Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering
    October 2023
    1809 pages
    ISBN:9798400708305
    DOI:10.1145/3650400
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 17 April 2024

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