Estrus detection in cattle is an important factor in livestock farming. With timely estrus detection, cattle are artificially fertilized and isolated for safety, which directly affects the productivity of livestock farms. Estrus can be successfully detected by identifying the mating posture of cattle. Therefore, in this paper, we propose the identification of cattle mating posture based on video inputs for prompt estrus detection. A deep learning-based object detection network that focuses on real-time processing with high processing speeds is applied. The use of deep learning-based object detection shows high accuracy, even with noise robustness. The performance of the network is improved through the inclusion of an additional layer and a new activation function. The composition of the additional layer enables training by extracting more features required for object detection. The application of the new activation function, Mish, which has a smoother curve, allows for better generalization and improves the accuracy of the results. The data needed for training were gathered by installing cameras at a livestock farm, and various datasets were used depending on camera placement. The results of this study were verified by the evaluation of four networks using test datasets containing image and video data from different environments. The identification of the mating posture of cattle attained 98.5% precision, 97.2% recall, and 97.8% accuracy.

牛发情检测是畜牧业的重要因素。通过及时发情检测，为安全起见，对牛进行了人工受精和隔离，这直接影响了牧场的生产力。通过确定牛的交配姿势可以成功地检测到发情期。因此，在本文中，我们提出了基于视频输入的牛交配姿势的识别，以用于迅速发情检测。应用了基于深度学习的对象检测网络，该网络专注于以高处理速度进行实时处理。基于深度学习的对象检测的使用显示出很高的准确性，即使具有噪声鲁棒性。通过添加附加层和新的激活功能，可以改善网络的性能。附加层的组成可通过提取对象检测所需的更多特征来进行训练。曲线更平滑的新激活函数Mish的应用可以更好地进行泛化并提高结果的准确性。培训所需的数据是通过在牲畜场安装摄像头收集的，并且根据摄像头的位置使用了各种数据集。通过使用包含来自不同环境的图像和视频数据的测试数据集对四个网络进行评估，验证了这项研究的结果。牛的交配姿势识别获得了98.5％的准确性，97.2％的召回率和97.8％的准确性。允许更好的概括并提高结果的准确性。培训所需的数据是通过在牲畜场安装摄像头收集的，并且根据摄像头的位置使用了各种数据集。通过使用包含来自不同环境的图像和视频数据的测试数据集对四个网络进行评估，验证了这项研究的结果。牛的交配姿势识别获得了98.5％的准确性，97.2％的召回率和97.8％的准确性。允许更好的概括并提高结果的准确性。培训所需的数据是通过在牲畜场安装摄像头收集的，并且根据摄像头的位置使用了各种数据集。通过使用包含来自不同环境的图像和视频数据的测试数据集对四个网络进行评估，验证了这项研究的结果。牛的交配姿势识别获得了98.5％的准确性，97.2％的召回率和97.8％的准确性。