基于改进ConvNeXt的奶牛行为识别方法

奶牛的动作行为（进食、躺卧、站立、行走和甩尾）直接或间接地反映了奶牛的健康及生理状况,是奶牛疾病监测及感知奶牛异常的关键，为准确高效地对奶牛行为进行识别，提出了一种融合时间和空间注意信息的多分支并行的CAFNet（ConvNeXt-ACM-FAM）奶牛行为识别模型，该模型在卷积网络ConvNeXt的基础上融合非对称多分支卷积模块（ACM）和特征注意力模块（FAM）。首先，利用ACM划分通道分支提取特征并保留一部分原始特征，防止信息过度丢失。其次，FAM对不同通道的特征进行融合并引入SimAM注意力机制，不增加网络参数的同时增强重要特征的有效提取。实验结果表明，该方法对进食、躺卧、站立、行走和甩尾行为识别准确率分别为95.50%、93.72%、90.26%、86.43%、89.39%，平均准确率为91.06%，参数量相较于原模型减少了1.5×106，浮点运算量减少了3×108，相较于其他模型，本文模型识别平均准确率平均提升8.63个百分点。本文研究成果可为奶牛疾病监测及预防提供技术支持。

Cow Behavior Recognition Method Based on Improved ConvNeXt

The behaviors of cows, including eating, lying, standing, walking and tail flicking, which directly or indirectly reflects the health and physiological condition of the cows. It is necessary to monitor cow diseases and detect anomalies in cow behavior. In order to achieve the goals, a multi-branch parallel CAFNet (ConvNeXt-ACM-FAM) cow behavior recognition model was proposed by combining temporal and spatial attention information. The model combined an asymmetric multi-branch convolutional module (ACM) and a feature attention module (FAM) on the basis of a ConvNeXt convolutional network. Firstly, ACM was utilized to partition channel branches for feature extraction, and retained some original features to prevent excessive information loss. And ACM can improve the running efficiency of the model. Secondly, FAM fused the features from different channels and introduced the SimAM attention mechanism, which enhanced the efficient extraction of important features without increasing network parameters and improved recognition accuracy. The result of experiment demonstrated that the CAFNet achieved recognition accuracy of the method for eating, lying, standing, walking, and tail flicking was 95.50%, 93.72%, 90.26%, 86.43%, and 89.39%, respectively. And the average recognition accuracy was 91.06%. Compared with the original model, the number of parameters was reduced by 1.5×106, the computational complexity was reduced by 3×108, and the average recognition accuracy was increased by 8.63 percentage points. The results can provide technical support for cow disease monitoring and prevention.