A CNN-Based Semi-supervised Self-training Method for Robust Underwater Fish Recognition
Authors: 
Tanqing Li, Zhili Zhao, Hengyu Zhang, Kun Li, Wenjun LvAuthors Info & Claims
Tanqing Li, Zhili Zhao, Hengyu Zhang, Kun Li, Wenjun LvAuthors Info & ClaimsPages 1553 - 1559
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
[1]
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
[2]
Boaz Zion, “The use of computer vision technologies in aquaculture – A review”. Computers and Electronics in Agriculture,01 Oct 2012, 88:125-132.
[3]
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
[4]
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.
[5]
Shasha Li; Yongjun Li; Yao Li; Mengjun Li; Xiaorong Xu, “YOLO-FIRI: Improved YOLOv5 for Infrared Image Object Detection”. 2021.
[6]
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
[7]
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.
[8]
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
Index Terms
- A CNN-Based Semi-supervised Self-training Method for Robust Underwater Fish Recognition
Recommendations
Inductive Semi-supervised Multi-Label Learning with Co-Training
KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningIn multi-label learning, each training example is associated with multiple class labels and the task is to learn a mapping from the feature space to the power set of label space. It is generally demanding and time-consuming to obtain labels for training ...
A debiased self-training framework with graph self-supervised pre-training aided for semi-supervised rumor detection
AbstractExisting rumor detection models have achieved remarkable performance in fully-supervised settings. However, it is time-consuming and labor-intensive to obtain extensive labeled rumor data. To mitigate the reliance on labeled data, semi-supervised ...
Highlights- A self-training framework for semi-supervised rumor detection is proposed.
- Graph self-supervised pre-training is employed to alleviate confirmation bias.
- Self-adaptive thresholds are designed to generate reliable pseudo-labels.
Bayesian Self-training for Semi-supervised 3D Segmentation
Computer Vision – ECCV 2024Abstract3D segmentation is a core problem in computer vision and, similarly to many other dense prediction tasks, it requires large amounts of annotated data for adequate training. However, densely labeling 3D point clouds to employ fully-supervised ...
Comments
Information & Contributors
Information
Published In
October 2023
1809 pages
ISBN:9798400708305
DOI:10.1145/3650400
Copyright © 2023 ACM.
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].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 17 April 2024
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
EITCE 2023
EITCE 2023: 2023 7th International Conference on Electronic Information Technology and Computer Engineering
October 20 - 22, 2023
Xiamen, China
Acceptance Rates
Overall Acceptance Rate 508 of 972 submissions, 52%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 22Total Downloads
- Downloads (Last 12 months)22
- Downloads (Last 6 weeks)5
Reflects downloads up to 25 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format