A Review of the Artificial Intelligence-based Image Classification of Fishes in the Global Oceans

ZHOU Peng; LI Changyong; BU Yuxin; ZHOU Zhinuo; WANG Chunsheng; SHEN Hongbin; PAN Xiaoyong

doi:10.11999/JEIT231365

Volume 46 Issue 5

May 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(5): 1853-1864

ZHOU Peng, LI Changyong, BU Yuxin, ZHOU Zhinuo, WANG Chunsheng, SHEN Hongbin, PAN Xiaoyong. A Review of the Artificial Intelligence-based Image Classification of Fishes in the Global Oceans[J]. Journal of Electronics & Information Technology, 2024, 46(5): 1853-1864. doi: 10.11999/JEIT231365

Citation:

ZHOU Peng, LI Changyong, BU Yuxin, ZHOU Zhinuo, WANG Chunsheng, SHEN Hongbin, PAN Xiaoyong. A Review of the Artificial Intelligence-based Image Classification of Fishes in the Global Oceans[J]. Journal of Electronics & Information Technology, 2024, 46(5): 1853-1864. doi: 10.11999/JEIT231365

Citation:

ZHOU Peng, LI Changyong, BU Yuxin, ZHOU Zhinuo, WANG Chunsheng, SHEN Hongbin, PAN Xiaoyong. A Review of the Artificial Intelligence-based Image Classification of Fishes in the Global Oceans[J]. Journal of Electronics & Information Technology, 2024, 46(5): 1853-1864. doi: 10.11999/JEIT231365

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A Review of the Artificial Intelligence-based Image Classification of Fishes in the Global Oceans

doi: 10.11999/JEIT231365

ZHOU Peng^{1, 2, 3},
LI Changyong³,
BU Yuxin^{1, 2},
ZHOU Zhinuo³,
WANG Chunsheng^{1, 2},
SHEN Hongbin³,
PAN Xiaoyong^{3
,
,}

1.
Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
2.
Key Laboratory of Marine Ecosystem Dynamics, Ministry of Natural Resources, Hangzhou 310012, China
3.
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China

Funds: The National Key Research and Development Program of China (2023YFC2811502), The Oceanic Interdisciplinary Program of Shanghai Jiao Tong University (SL2022ZD108, SL2021MS005)

Received Date: 2023-12-11
Rev Recd Date: 2024-03-29

Available Online: 2024-04-12

Publish Date: 2024-05-30

Abstract

Abstract

Understanding the species composition, abundance and temporal and spatial distribution of fish on a global scale will help their biodiversity conservation. Underwater image acquisition is one of the main means to survey fish species diversity, but image data analysis is time-consuming and labor-intensive. Since 2015, a series of progress has been made in updating the datasets of marine fish images and optimizing the algorithm of deep learning models, but the performance of fine-grained classification is still insufficient, and the production practice application of research results is relatively weak. Therefore, the need for automated fish image classification in marine investigations is firstly studied. Then a comprehensive introduction to fish image datasets and deep learning algorithm applications is provided, and the main challenges and the corresponding solutions are analyzed. Finally, the importance of automated classification of marine fish images for related image information processing research is discussed, and its prospects in the field of marine monitoring are summarized.
- Deep learning,
- Marine fish,
- Biodiversity,
- Few shots,
- Image classification

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References(64)

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