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Computer Vision Models in Intelligent Aquaculture with Emphasis on Fish Detection and Behavior Analysis: A Review

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Abstract

Intelligence technologies play an important role in increasing product quality and production efficiency in digital aquaculture. Automatic fish detection will contribute to achieving intelligent production and scientific management in precision farming. Due to the availability and ubiquity of modern information technology, such as the internet of things, big data, and camera devices, computer vision techniques, as an essential branch of artificial intelligence, have emerged as a powerful tool for achieving automatic fish detection. At present, it has been widely used in fish species identification, counting, and behavior analysis. Nevertheless, computer vision modeling used for fish detection is riddled with many challenges, such as varies in illumination, low contrast, high noise, fish deformation, frequent occlusion, and dynamic background. Hence, this paper provides a comprehensive review of the computer vision model for fish detection under unique application scenarios. Firstly, the image acquisition system based on 2D and 3D is discussed. Further, many fish detection techniques are categorized as appearance-based, motion-based, and deep learning. In addition, applications of fish detection and public open-source datasets are also presented in the literature. Finally, the prominent findings and the directions of future research are addressed toward the advancement in the aquaculture field throughout the discussion and conclusion section.

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Acknowledgements

This work was supported by the National Key R&D Program of China “Next generation precision aquaculture: R&D on intelligent measurement, control and equipment technologies” (No. 2017YFE0122100), and the Key R&D Program “Research and development of intelligent model and precise monitoring of shrimp processing” (No. 2018YFD0700904-2).

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  1. College of Information and Electrical Engineering, China Agricultural University, 17 Tsinghua East Road, Beijing, 100083, People’s Republic of China

    Ling Yang, Yeqi Liu, Xiaomin Fang, Lihua Song, Daoliang Li & Yingyi Chen

  2. National Innovation Center for Digital Fishery, China Agricultural University, 17 Tsinghua East Road, Beijing, 100083, People’s Republic of China

    Ling Yang, Yeqi Liu, Xiaomin Fang, Lihua Song, Daoliang Li & Yingyi Chen

  3. Beijing Engineering and Technology Research Centre for the Internet of Things in Agriculture, China Agricultural University, 17 Tsinghua East Road, Beijing, 100083, People’s Republic of China

    Ling Yang, Yeqi Liu, Lihua Song & Yingyi Chen

  4. Precision Agricultural Technology Integration Research Base (Fishery), Ministry of Agriculture and Rural Affairs, China Agricultural University, 17 Tsinghua East Road, Beijing, 100083, People’s Republic of China

    Xiaomin Fang & Daoliang Li

  5. School of Information Science and Technology, Beijing Forestry University, Beijing, 100083, People’s Republic of China

    Huihui Yu

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Yang, L., Liu, Y., Yu, H. et al. Computer Vision Models in Intelligent Aquaculture with Emphasis on Fish Detection and Behavior Analysis: A Review. Arch Computat Methods Eng 28, 2785–2816 (2021). https://doi.org/10.1007/s11831-020-09486-2

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