Fish Feeding Behavior Recognition Based on Enhanced MobileViTv3 Model

Video stream-based fish feeding behavior recognition has garnered significant attention in recent years, accelerating the optimization of feeding strategies and enhancing aquaculture efficiency. However, current feeding intensity assessment methods suffer from inefficiency and subjectivity in manual observation, compounded by challenges in accurately extracting behavioral features due to high mobility and random movement patterns of outdoor-cultured fish. Constructing an efficient multi-feature extraction model for fish feeding recognition—particularly deployable on mobile and edge devices—remains a critical challenge. To address these limitations, this paper proposes a multi - feature extraction network based on improved MobileViT V3,which uses video streams as input and solves problems of large model size, high computational complexity, and insufficient feature extraction in current models, integrating three key innovations: (1) A Multi-Scale Convolution Module (MSCM) that concurrently captures spatiotemporal, motion, and channel features from video streams; (2) A Feature Fusion Convolutional Block Attention Module (FCBAM) combining shallow-deep features with adaptive attention weighting; (3) A BiasLoss function with dynamic scaling to address intra-class variation and low-quality data. Evaluated on grass carp and crucian carp, our model achieves 97.7% accuracy in feeding intensity classification with only (5.8) M parameters, outperforming C3D-ConvLSTM and MobileNetv3-small baselines while demonstrating enhanced robustness for edge deployment.