摘要： 实际生产中，对鱼类的投喂控制仍是以人工经验判断和时序控制为主，易造成饲料浪费和环境污染。实时检测鱼群摄食强度，可用于指导投喂，进而提高饲料利用率，并降低残饵污染。基于此，本研究提出一种基于机器视觉和轻量型S3D算法的鱼群摄食强度实时识别算法，可精确定位视频流中 “强、中、弱、无” 四种鱼群摄食强度状态。其首先将I3D网络作为基准，使用Inception模块和深度可分离卷积构建3D时空Sep-Inc模块；其次，利用3D时空Sep-Inc模块、池化层和3D卷积层交替搭建轻量型S3D网络。最后，开发了基于PyQt5的金鳟鱼摄食强度识别系统。结果表明：S3D算法对四类摄食强度的识别准确率可达92.68%，比C3D和R2+1D算法分别提高9.75% 和14.15%，同时Parameters参数和GFLOPs参数也大幅下降，识别摄食强度标签的速率达到17f/s。研究表明，本算法不仅适用于金鳟，也有望适用于其他游泳型鱼类，并可提供投喂决策建议。

关键词: 水产养殖, 轻量型S3D算法, 摄食强度识别, 鱼类投喂

Abstract: In the current production process, the feeding control of fish is still based on artificial experience judgment and time sequence, which is easy to cause bait waste and environmental pollution. The real-time detection of the feeding intensity of fish can be used to guide feeding, thus improving the bait utilization rate and reducing residual bait pollution. Based on this, this paper proposes a real-time detection algorithm of fish feeding intensity based on machine vision and a lightweight S3D algorithm, which can accurately locate the four intensity levels of "strong, medium, weak, and none" in the video. Firstly, the 3D spatiotemporal Sep-Inc module is proposed using the I3D network as the benchmark, the inception module, and the depth separable convolution. Secondly, a lightweight S3D network is formed by alternately building a 3D  Spatio-temporal SEP Inc module, pooling layer, and 3D convolution layer. Finally, the feeding intensity identification system of golden trout was developed by using PyQt5. The experimental results show that the identification accuracy of the S3D algorithm for four types of feeding intensity reaches 92.68%, which is 9.75% and 14.15% higher than that of C3d and R2 + 1D algorithms respectively. Meanwhile, the parameters and GFLOPs parameters are also greatly reduced, and the feeding intensity tags identified per second reach 17F / s. Research shows that the method is not only applicable to golden trout but more importantly, it is expected to apply to the breeding of other swimming fish and provide feeding decision suggestions for intelligent feeding systems.

Key words: aquaculture, lightweight S3D algorithm, feeding intensity identification, fish feeding