Using channel pruning–based YOLOv5 deep learning algorithm for accurately counting fish fry in real time-Reference-Cited by-同舟云学术

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Using channel pruning–based YOLOv5 deep learning algorithm for accurately counting fish fry in real time

Published:2024-07-08 Issue: Volume: Page:
ISSN:0967-6120
Container-title:Aquaculture International
language:en
Short-container-title:Aquacult Int

Author:

Xu Huanliang,Chen Xi,Wu Yuqiang,Liao Bowen,Liu Longshen,Zhai Zhaoyu

Funder

Jiangsu Province Modern Agricultural Machinery Equipment and Technology Demonstration and Promotion Project

the Startup Foundation of New Professor at Nanjing Agricultural University

Fundamental Research Funds for the Central Universities

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10499-024-01609-x.pdf

Reference41 articles.

1. Abreu SN, Jesus F, Domingues I et al (2022) Automated counting of daphnid neonates, Artemia nauplii, and zebrafish eggs: a proof of concept. Environ Toxicol Chem 41(6):1451–1458

2. Burnett MJ, Süßle V, Saayman T et al (2024) Detecting changes in fish behaviour in real time to alert managers to thresholds of potential concern. River Res Appl 40(1):129–147

3. Gong B, Dai K, Shao J et al (2023) Fish-TViT: a novel fish species classification method in multi water areas based on transfer learning and vision transformer. Heliyon 9(6):e16761

4. Guan Y, Liu N, Zhao P et al (2022) DAIS: automatic channel pruning via differentiable annealing indicator search. IEEE Trans Neural Netw Learn Syst 34(12):9847–9858

5. Han S, Pool J, Tran J et al (2015) Learning both weights and connections for efficient neural network. arXiv.1-9

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