Soft-shell crab detection model based on YOLOF-Reference-Cited by-同舟云学术

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Soft-shell crab detection model based on YOLOF

Published:2024-02-15 Issue:4 Volume:32 Page:5269-5298
ISSN:0967-6120
Container-title:Aquaculture International
language:en
Short-container-title:Aquacult Int

Author:

Zhang Zhen,Liu Feifei,He Xinfeng,Wu Xinyu,Xu Meijuan,Feng Shuai

Funder

Jiangxi Province Key Innovation Research Development Platform Plan, China

National Science Foundation for Young Scientists of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10499-024-01426-2.pdf

Reference51 articles.

1. Aqua Detect (2022) Shrimps dataset. Roboflow. https://universe.roboflow.com/aqua-detect-hz5fh/shrimps-zvpaz. Accessed Jan 2024

2. Benjakul S, Sutthipan N (2009) Comparative study on chemical composition, thermal properties and microstructure between the muscle of hard shell and soft shell mud crabs. Food Chem 1123:627–633. https://doi.org/10.1016/j.foodchem.2008.06.019

3. Bochkovskiy A, Wang CY, Liao HYM (2020) YOLOv4: optimal speed and accuracy of object detection. arXiv 2004:10934. https://doi.org/10.48550/arXiv.2004.10934

4. Cao S, Zhao D, Liu X, Sun Y (2020) Real-time robust detector for underwater live crabs based on deep learning. Comput Electron Agric 172:105339. https://doi.org/10.1016/j.compag.2020.105339

5. Chen J, Kao SH, He H, Zhuo W, Wen S, Lee CH, Gary Chan SH (2023) Run, Don’t Walk: Chasing Higher FLOPS for Faster Neural Networks. In: Proceedings of the 2023 IEEE computer society conference on computer vision and pattern recognition (CVPR):12021–12031. https://doi.org/10.1109/CVPR52729.2023.01157

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