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

被引：0
|
作者：
Zhou P. [1 ,2 ]
Li C. [3 ]
Bu Y. [1 ,2 ]
Zhou Z. [3 ]
Wang C. [1 ,2 ]
Shen H. [3 ]
Pan X. [3 ]
机构：
[1] Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou
[2] Key Laboratory of Marine Ecosystem Dynamics, Ministry of Natural Resources, Hangzhou
[3] Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2024年 / 46卷 / 05期
关键词：
Biodiversity; Deep learning; Few shots; Image classification; Marine fish;
D O I：
10.11999/JEIT231365
中图分类号：
学科分类号：
摘要：
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. © 2024 Science Press. All rights reserved.
引用
收藏
页码：1853 / 1864
页数：11
相关论文
共 64 条
  • [1] KUITER R H, DEBELIUS H., World atlas of marine fishes, (2006)
  • [2] ALLEN G R., Field Guide to Marine Fishes of Tropical Australia and South-East Asia, (2009)
  • [3] CHEN Dagang, ZHANG Meizhao, Marine Fishes of China, (2015)
  • [4] CHEN Dagang, ZHANG Meizhao, Marine Fishes of China, (2015)
  • [5] CHEN Dagang, ZHANG Meizhao, Marine Fishes of China, (2015)
  • [6] LI Xiu, SHANG Min, QIN Hongwei, Et al., Fast accurate fish detection and recognition of underwater images with Fast R-CNN[C], pp. 1-5, (2015)
  • [7] VILLON S, CHAUMONT M, SUBSOL G, Et al., Coral reef fish detection and recognition in underwater videos by supervised machine learning: Comparison between Deep Learning and HOG+SVM methods[C], The 17th International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 160-171, (2016)
  • [8] JAGER J, SIMON M, DENZLER J, Et al., Croatian fish dataset: Fine-grained classification of fish species in their natural habitat[C], Machine Vision of Animals and their Behaviour Workshop, 2015, (2015)
  • [9] SALEH A, LARADJI I H, KONOVALOV D A, Et al., A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis, Scientific Reports, 10, 1, (2020)
  • [10] LIN Yuan, CHU Zhaoqi, KORHONEN J, Et al., Fast accurate fish recognition with deep learning based on a domain-specific large-scale fish dataset[C], The 29th International Conference on Multimedia Modeling, pp. 515-526, (2023)
1234567