基于改进YOLO v8关键点检测的西门塔尔牛只个体识别方法

Individual Identification Method of Simmental Cattle Based on Improved YOLO v8 Keypoint Detection

摘要:

随着畜牧业的智能化发展，基于计算机视觉技术的牛只个体识别在牛场养殖中扮演着越来越重要的角色。然而，在实际应用中，牛脸角度的快速变化、牛只数量的频繁变动以及模型识别速度的局限性严重影响了识别的准确性和效率。为了应对这些挑战，本文提出了一种基于改进YOLO v8关键点检测的西门塔尔牛个体识别方法。该方法包括2个阶段，在牛脸检测阶段，改进YOLO v8模型自动检测牛脸图像中的关键点，并进行矫正对齐和背景裁剪，使牛脸图像角度趋于一致。在牛脸识别阶段，引入了深度可分离卷积以减少参数量，利用残差连接和注意力机制增强了模型的特征提取能力，从而提升了识别准确性和速度。为验证方法的有效性，构建了包含159头西门塔尔牛的牛脸图像数据集。实验结果表明，本文模型在参数量和推理时间上有显著改进，单幅图像处理时间缩短至39ms以内，识别准确率为95.8%，比其他模型相比平均提高4.8个百分点。此外，本文方法支持实时更新牛只数据库，在增加牛脸图像后，仍能保持较高准确率。

Abstract:

With the intelligent development of animal husbandry, computer-vision-based individual cattle recognition is playing an increasingly important role in modern cattle farming. However, in practical applications, the rapid changes in the angles of cattle faces, frequent fluctuations in the number of cattle, and limitations in model recognition speed severely affect the accuracy and efficiency of recognition. To address these challenges, an individual recognition method for Simmental cattle was proposed based on improved YOLO v8 keypoint detection. The method consisted of two stages: in the cattle face detection stage, the improved YOLO v8 model automatically detected keypoints in cattle face images and performed correction alignment and background cropping to make the angles of the cattle face images more consistent. In the cattle face recognition stage, depthwise separable convolution was firstly introduced to reduce the number of parameters, and then residual connections and attention mechanisms were utilized to enhance the model’s feature extraction capabilities, thereby improving recognition accuracy and speed. To validate the effectiveness of the method, a dataset of cattle face images containing 159 Simmental cattle was constructed. Experimental results showed that the proposed model had significant improvements in the number of parameters and inference speed, with processing time for a single image reduced to within 39ms and a recognition accuracy of 95.8%, an average improvement of 4.8 percentage points compared with that of other models. Additionally, the proposed method supported real-time updates to the cattle database, maintaining high accuracy even after adding new cattle face images.