Fish Image Classification Based on MobileNetV2 with Transfer Learning Technique for Robotic Application in Aquaculture
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-71360-6_15
Reference27 articles.
1. Vo, T., Ko, H., Huh, J.-H., Kim, Y.: Overview of smart aquaculture system: focusing on applications of machine learning and computer vision. Electronics 10(22) (2021). https://doi.org/10.3390/electronics10222882
2. Le, V., Ronzhin, A.: Review of intelligent control systems and robotic challenges in aquaculture production. Mar. Intell. Technol. 1(1), 171–180 (2024). (In Russian). https://doi.org/10.37220/MIT.2024.63.1.021
3. Le, V., Ronzhin, A.: Methods and technical means of positioning and navigation of robots in the aquatic environment. News Kabardino-Balkarian Sci. Center Russ. Acad. Sci. 6(116), 167–178 (2023). (In Russian). https://doi.org/10.35330/1991-6639-2023-6-116-167-178
4. Yoshua, B.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009). https://doi.org/10.1561/2200000006
5. Kratzert, F., Mader, H.: Fish species classification in underwater video monitoring using Convolutional Neural Networks (2018). https://doi.org/10.31223/osf.io/dxwtz
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