兰州大学机构库
A CNN-Based Semi-supervised Self-training Method for Robust Underwater Fish Recognition
Li, Tanqing1; Zhao, ZL(赵志立)1; Zhang, Hengyu2; Li, Kun3; Lv, Wenjun3
2023-10-20
Source PublicationACM International Conference Proceeding Series   Impact Factor & Quartile Of Published Year  The Latest Impact Factor & Quartile
Conference Name2023 7th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2023
Conference DateOctober 20, 2023 - October 22, 2023
Pages1553-1559
AbstractRecent 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. © 2023 ACM.
PublisherAssociation for Computing Machinery
DOI10.1145/3650400.3650660
Indexed ByEI
Language英语
EI Accession Number20241816005549
Original Document TypeConference article (CA)
Conference PlaceXiamen, China
Citation statistics
Document Type会议论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/588979
Collection兰州大学
信息科学与工程学院
Affiliation
1.School of Information Science and Engineering, Lanzhou University, Lanzhou, China;
2.University College London, Gower St, London; WC1E 6BT, United Kingdom;
3.Department of Automation, Institute of Advanced Technology, University of Science and Technology of China, Hefei, China
First Author AffilicationSchool of Information Science and Engineering
First Signature AffilicationSchool of Information Science and Engineering
Recommended Citation
GB/T 7714
Li, Tanqing,Zhao, Zhili,Zhang, Hengyu,et al. A CNN-Based Semi-supervised Self-training Method for Robust Underwater Fish Recognition[C],2023:1553-1559.
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