Cattle behavior recognition based on feature fusion under a dual attention mechanism

Abstract

In recent years, artificial intelligence has been widely used in such fields as agricultural informatization, precision agriculture and precision animal husbandry. Due to limited research on deep learning in real-time agricultural and pastoral situations, deep learning and computer vision have become very important topics in the agricultural field. Recent studies have shown that the fusion of features under different attention mechanisms will help advance the utilization of such features, and will thus influence the accuracy and generalization ability of the models used. In this paper, we propose a lightweight network structure based on feature fusion under a dual attention mechanism with the same activation and joint loss functions. More specifically, we propose an innovative method to improve the network structure of two different attention mechanisms, and achieve feature fusion by combining the two. At the same time, we keep the activation functions consistent with those of the original network structure, and we develop a joint loss function to expand the use of various features. We also take the novel approach of applying the trajectory behavior analysis method to walking and standing. Experiments using both a publicly available data set and a data set obtained from a farm show that our algorithm achieves state-of-the-art performance in terms of accuracy and generalization ability, as compared to other methods.