人工神经网络
人工智能
模式识别(心理学)
系列(地层学)
计算机科学
时间序列
计算机视觉
机器学习
生物
古生物学
作者
Yan Zhang,Xinze Yang,Yufei Liu,Junyu Zhou,Yihong Huang,Jiapeng Li,Longxiang Zhang,Qin Ma
标识
DOI:10.1016/j.compag.2024.108710
摘要
With the development of modern animal husbandry, especially pig farming, large-scale, intensive, and automated farming has become the trend in the industry. The realization of accurate recognition and warning of dangerous actions in feeding scenarios for sows and piglets holds high research and practical value in this field. This paper addresses this issue by proposing a Transformer-based Neural Network (TNN) model. This model emphasizes the extraction of global features and handling of long-distance dependencies, significantly improving the accuracy of behavior recognition. Compared with traditional neural network models, TNN demonstrates superiority in dealing with animal behavior recognition in farming scenarios. Furthermore, an in-depth study of the attention mechanism in the TNN model was conducted in this paper. By visualizing the attention heat map of the TNN model, it was found that TNN could effectively focus on key areas in the image, thereby accurately identifying the behavior of piglets. Finally, this paper proposes a unique model lightweighting strategy that allows the TNN model to run efficiently on edge devices. In the experimental part, the performance of the TNN model on five behavior recognition tasks was evaluated first. The results showed that the TNN model achieved high scores in both Precision and Recall, far exceeding traditional neural network models. Then, by visualizing the attention heat map of the TNN model, the superiority of the TNN model in focusing on key image areas was further confirmed. In the end, the effect of the model lightweighting strategy was demonstrated, and even with a significant reduction in parameters and computational complexity, the performance of the TNN model remained excellent. This research not only promotes the development of animal behavior recognition technology but also provides new insights and tools for the precise management and efficient operation of the animal husbandry industry.
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