A Time-Series Neural Network for Pig Feeding Behavior Recognition and Dangerous Detection from Videos

作     者：Zhang, Yan Yang, Xinze Zhou, Junyu Li, Jiapeng Zhang, Longxiang Ma, Qin 

作者机构：College of Information and Electrical Engineering China Agricultural University Beijing100083 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Department of Communication University of Colorado Denver Denver United States School of Computer Science and Engineering Beihang University Beijing China Fu Foundation School of Engineering and Applied Science Columbia University the City of New York New York City United States 

出 版 物：《SSRN》 

年 卷 期：2023年

核心收录：

主　　题：Neural network models 

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. © 2023, The Authors. All