基于帧间深度特征差分的大西洋鲑鱼群活跃度分类模型

鱼群活跃度是鱼类健康福利养殖的特征性指标之一，实现鱼群活跃度细粒度分类有利于更精细地描述鱼群健康状况、评估鱼群福利水平。基于工厂化循环水养殖系统，本文建立了水下大西洋鲑鱼群活跃度细粒度分类视频数据集，并提出一种基于帧间深度特征差分的鱼群活跃度分类模型，通过引入残差连接的小型卷积神经网络提取视频帧的特征，进而在相邻帧之间做差分运算和平方运算得到视频帧间特征，最后将其输入基于外部注意力机制的分类网络IFDNet中得到视频类别。试验结果表明，本文提出的CNN-IFDNet模型分类准确率达到97.72%,F1值达到97.42%,以较低的计算复杂度实现了对水下视频鱼群活跃度的三分类。相较于实验室环境，基于真实养殖环境对鱼群活跃度所展开的算法研究实际应用性更强，可以为精细化描述鱼群的活跃度、实现智能监测鱼类健康状况提供参考，帮助养殖人员发现并排除导致鱼群活跃度异常的水质环境、病害等因素。

Classification Model of Atlantic Salmon Activity Intensity Based on Deep Feature Differencing between Frames

Fish activity intensity is one of the characteristic indicators of fish health and welfare farming. The fine-grained classification of fish activity intensity is beneficial to describe fish health status and assess fish welfare levels. The fine-grained classification of Atlantic salmon activity intensity where a small scaled underwater video dataset was collected in the industrial recirculating aquaculture system was carried out. Firstly, the features of video frames were extracted through a small convolutional neural network with residual connections. Then the inter-frame features were obtained by performing differential and square operations between adjacent frames. Finally, the inter-frame features were inputted into the classification network IFDNet based on the external attention mechanism to obtain the video category. The experimental results showed that the classification accuracy of the CNN-IFDNet model proposed reached 97.72%, and the F1 score reached 97.42%. With low computational complexity, the three classification of the fish activity intensity video was realized. Compared with the laboratory environment, the algorithm research based on the real farming environment for fish activity intensity was more practical. The research result can provide a reference for elaborately describing the activity intensity of fish school and realizing intelligent monitoring of fish health status, which can help aquaculture workers discover abnormal conditions and investigate factors causing abnormal fish activity intensity, such as water quality environment and diseases.