Video monitoring systems are successfully widely used in many on-land artificial intelligence applications. They have been introduced into fishery production in recent years, such as video-based live fish detection and biomass estimation. Such ways help protect the sea environment by avoiding overstocking and pollution by ocean disasters or human mistakes in the production. However, underwater detection and segmentation are now still challenging because of the complex and volatile environment. The paper proposes an efficient underwater fish school segmentation framework for live fish detection and counting in the high-density cage. Adaptive multi-scale Gaussian background models are first constructed frame by frame to separate the foreground fish groups from the background seawater. The fish groups are then divided into individual fish by density estimation using directional weighted convolution kernels. No other underwater video pre-processing algorithms are introduced in the framework. The framework only needs real-time video frames as input. It uses online segmentation algorithms to detect and count live fish. No other pre-collected labeled videos are used to train and fine-tune the framework. It shows robust detection and statistics results in a natural aquaculture deep-sea cage.

视频监控系统成功地广泛应用于许多陆上人工智能应用。近年来，它们已被引入渔业生产，例如基于视频的活鱼检测和生物量估计。这种方式有助于保护海洋环境，避免海洋灾害或人为错误造成的库存积压和污染。然而，由于环境复杂多变，水下检测和分割现在仍然具有挑战性。本文提出了一种高效的水下鱼群分割框架，用于高密度网箱中的活鱼检测和计数。首先逐帧构建自适应多尺度高斯背景模型，将前景鱼群与背景海水分开。然后使用方向加权卷积核通过密度估计将鱼群分成单独的鱼。框架中没有引入其他水下视频预处理算法。该框架只需要实时视频帧作为输入。它使用在线分割算法来检测和计数活鱼。没有其他预先收集的标记视频用于训练和微调框架。它显示了天然水产养殖深海网箱的强大检测和统计结果。没有其他预先收集的标记视频用于训练和微调框架。它显示了天然水产养殖深海网箱的强大检测和统计结果。没有其他预先收集的标记视频用于训练和微调框架。它显示了天然水产养殖深海网箱的强大检测和统计结果。