关键词: 鱼类, 计算机视觉, 摄食行为, 饵料检测, 光流法, 深度学习, 智能投喂

Abstract: Feeding is one of the main tasks in aquaculture, and how to reduce the cost of feeding is the key point of maximizing the profits of aquaculture. Using computer vision technology to monitor the feeding behavior of fish and quantify the intensity of fish feeding desire can realize automatic feeding on demand, reduce feed waste, and improve feed utilization. This paper reviews the research progress of four kinds of fish intelligent feeding methods based on computer vision, such as food detection, optical flow method, texture and deep learning, and analyzes the advantages and disadvantages of each method. Bait detection method is simple and easy to implement, but it is difficult to identify the residual bait accurately. Optical flow method can capture fish movement information effectively, but it is easily affected by environment and light. Texture and other feature methods use more types of features and more effective information, but they are not suitable for high-density culture. Deep learning method has high recognition accuracy, strong robustness, large calculation amount, and high requirements for equipment computing power. Based on this, three research directions of large-scale data set, efficient lightweight depth model and “Internet of Things +” intelligent feeding are proposed. It provides reference for further improving the maturity and practicability of intelligent feeding method.

Key words: fish, computer vision, feeding behavior, feed detection, optical flow, deep learning, intelligent feeding