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.

智能技术在提高数字水产养殖的产品质量和生产效率方面发挥着重要作用。鱼的自动检测将有助于实现精确养殖中的智能生产和科学管理。由于物联网，大数据和摄像头设备等现代信息技术的可用性和普遍性，计算机视觉技术已成为人工智能的重要分支，已成为实现鱼类自动检测的强大工具。目前，它已被广泛用于鱼类的鉴定，计数和行为分析。尽管如此，用于鱼类检测的计算机视觉建模仍然面临许多挑战，例如光照变化，低对比度，高噪声，鱼类变形，频繁遮挡和动态背景。因此，本文对独特的应用场景下鱼类检测的计算机视觉模型进行了全面综述。首先，讨论了基于2D和3D的图像采集系统。此外，许多鱼类检测技术被分类为基于外观，基于运动和深度学习。此外，文献中还介绍了鱼类检测和公共开源数据集的应用。最后，在整个讨论和结论部分中，着重论述了水产养殖领域的重大发现和未来研究的方向。许多鱼类检测技术分为基于外观，基于运动和深度学习。此外，文献中还介绍了鱼类检测和公共开源数据集的应用。最后，在整个讨论和结论部分中，着重论述了水产养殖领域的重大发现和未来研究的方向。许多鱼类检测技术分为基于外观，基于运动和深度学习。此外，文献中还介绍了鱼类检测和公共开源数据集的应用。最后，在整个讨论和结论部分中，着重论述了水产养殖领域的重大发现和未来研究的方向。