基于轴向特征校准和时间段网络的鱼群摄食强度分类模型研究

Research on fish feeding intensity classification model based on axial feature calibration and temporal segment network

  • 摘要: 传统的水产养殖通过定量投喂或依靠人工经验投放饲料,易导致喂食不均、饲料浪费和环境污染等问题。旨在设计一种用于识别鱼群摄食强度的模型以提升投喂效率,减少污染。以时间段网络作为基础模型,捕捉鱼群长时间序列的摄食行为变化,加入时间移位操作,更细致地捕捉视频相邻帧之间的动态变化特征,并通过轴向特征校准自适应地调整特征,使模型能够更加精确地关注到鱼群摄食行为的不同轴向变化特征。实验表明,设计的模型相较于二维卷积网络 (TSN),平均准确率提升10.0%,参数量仅提升5.2%;相较于三维卷积网络 (C3D),平均准确率提升0.9%,参数量下降67.3%;相较于以Transformer为架构的Swin Transformer模型,平均准确率提升4.1%,参数量下降9.2%。表明设计的模型对于鱼群摄食强度识别分类更加准确,为制定鱼群的精确投喂策略提供了科学依据。

     

    Abstract: Traditional aquaculture methods rely on either quantitative feeding or human experience, often resulting in uneven feeding, feed waste and environmental pollution. This study aims to design a model for identifying the feeding intensity to enhance feeding efficiency and reduce pollution. The temporal segment network was employed as the base model to capture long-term changes in fish feeding behavior. Temporal shift operations were introduced to more accurately capture the dynamic changes between adjacent video frames. Through axial feature calibration, the model adaptively adjusted its features, enabling more precise focus on the variations in different axial features of fish feeding behavior. Experimental results indicate that compared with the two-dimensional convolutional network (TSN), the proposed model improved average accuracy by 10.0% with only a 5.2% increase in parameters. Compared with the three-dimensional convolutional network (C3D), it achieved a 0.9% accuracy improvement while reducing parameters by 67.3%. Additionally, compared with the Swin Transformer model based on the Transformer architecture, it increased average accuracy by 4.1% while reducing parameters by 9.2%. The findings demonstrate that the model we designed is more accurate in identifying and classifying the feeding intensity of fish schools, providing a scientific basis for formulating precise feeding strategies for fish schools.

     

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