The accurate identification of individual sheep is a crucial prerequisite for establishing digital sheep farms and precision livestock farming. Currently, deep learning technology provides an efficient and non-contact method for sheep identity recognition. In particular, convolutional neural networks (CNNs) can be used to learn features of sheep faces to determine their corresponding identities. However, the existing sheep face recognition models face problems such as large model size, and high computational costs, making it difficult to meet the requirements of practical applications. In response to these issues, we introduce a lightweight sheep face recognition model called YOLOv7-Sheep Face Recognition (YOLOv7-SFR). Considering the labor-intensive nature associated with manually capturing sheep face images, we developed a face image recording channel to streamline the process and improve efficiency. This study collected facial images of 50 Small-tailed Han sheep through a recording channel. The experimental sheep ranged in age from 1 to 3 years, with an average weight of 63.1 kg. Employing data augmentation methods further enhanced the original images, resulting in a total of 22,000 sheep face images. Ultimately, a sheep face dataset was established. To achieve lightweight improvement and improve the performance of the recognition model, a variety of improvement strategies were adopted. Specifically, we introduced the shuffle attention module into the backbone and fused the Dyhead module with the model's detection head. By combining multiple attention mechanisms, we improved the model's ability to learn target features. Additionally, the traditional convolutions in the backbone and neck were replaced with depthwise separable convolutions. Finally, leveraging knowledge distillation, we enhanced its performance further by employing You Only Look Once version 7 (YOLOv7) as the teacher model and YOLOv7-SFR as the student model. The training results indicate that our proposed approach achieved the best performance on the sheep face dataset, with a mean average precision@0.5 of 96.9%. The model size and average recognition time were 11.3MB and 3.6 ms, respectively. Compared to YOLOv7-tiny, YOLOv7-SFR showed a 2.1% improvement in mean average precision@0.5, along with a 5.8% reduction in model size and a 42.9% reduction in average recognition time. The research results are expected to drive the practical applications of sheep face recognition technology.

中文翻译：

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基于人脸图像记录通道的轻量级模型羊脸识别

羊个体的准确识别是建立数字化养羊场和精准畜牧业的重要前提。目前，深度学习技术为羊身份识别提供了一种高效、非接触的方法。特别是，卷积神经网络（CNN）可用于学习羊脸的特征以确定其相应的身份。然而，现有的羊脸识别模型存在模型规模大、计算成本高等问题，难以满足实际应用的要求。针对这些问题，我们引入了一种轻量级的羊脸识别模型，称为YOLOv7-Sheep Face Recognition（YOLOv7-SFR）。考虑到手动捕捉羊脸图像的劳动密集性，我们开发了人脸图像记录通道来简化流程并提高效率。本研究通过录音通道采集了50只小尾寒羊的面部图像。实验羊年龄1～3岁，平均体重63.1公斤。采用数据增强方法进一步增强了原始图像，总共产生了 22,000 张羊脸图像。最终建立了羊脸数据集。为了实现轻量级改进，提高识别模型的性能，采用了多种改进策略。具体来说，我们将 shuffle 注意力模块引入到主干中，并将 Dyhead 模块与模型的检测头融合。通过结合多种注意力机制，我们提高了模型学习目标特征的能力。此外，骨干和颈部的传统卷积被深度可分离卷积取代。最后，利用知识蒸馏，我们采用 You Only Look Once version 7 (YOLOv7) 作为教师模型，YOLOv7-SFR 作为学生模型，进一步增强了其性能。训练结果表明，我们提出的方法在羊脸数据集上取得了最佳性能，平均精度@0.5为96.9%。模型大小和平均识别时间分别为 11.3MB 和 3.6 ms。与 YOLOv7-tiny 相比，YOLOv7-SFR 在平均精度@0.5 上提高了 2.1%，模型大小减少了 5.8%，平均识别时间减少了 42.9%。研究成果有望推动羊脸识别技术的实际应用。