基于YOLO v8n-seg-FCA-BiFPN的奶牛身体分割方法

奶牛身体部位的精准分割广泛应用于奶牛体况评分、姿态检测、行为分析及体尺测量等领域。受奶牛表面污渍和遮挡等因素的影响，现有奶牛部位精准分割方法实用性较差。本研究在YOLO v8n-seg模型的基础上，加入多尺度融合模块与双向跨尺度加权特征金字塔结构，提出了YOLO v8n-seg-FCA-BiFPN奶牛身体部位分割模型。其中，多尺度融合模块使模型更好地提取小目标几何特征信息，双向跨尺度加权特征金字塔结构实现了更高层次的特征融合。首先在奶牛运动通道处采集奶牛侧面图像作为数据集，为保证数据集质量，采用结构相似性算法剔除相似图像，共得到1452幅图像。然后对目标奶牛的前肢、后肢、乳房、尾部、腹部、头部、颈部和躯干8个部位进行标注并输入模型训练。测试结果表明，模型精确率为96.6%，召回率为94.6%，平均精度均值为97.1%，参数量为3.3×106，检测速度为6.2f/s。各部位精确率在90.3%~98.2%之间，平均精度均值为96.3%。与原始YOLO v8n-seg相比，YOLO v8n-seg-FCA-BiFPN的精确率提高3.2个百分点，召回率提高2.6个百分点，平均精度均值提高3.1个百分点，改进后的模型在参数量基本保持不变的情况下具有更强的鲁棒性。遮挡情况下该模型检测结果表明，精确率为93.8%，召回率为91.67%，平均精度均值为93.15%。结果表明，YOLO v8n-seg-FCA-BiFPN网络可以准确、快速地实现奶牛身体部位精准分割。

Segmentation Model of Cow Body Parts Based on YOLO v8n-seg-FCA-BiFPN

The fine segmentation of cow body parts has significant applications in research fields such as cow body condition scoring, posture estimation, behavior recognition, and body measurement. Due to the limited practicality of existing segmentation methods for different cow body parts, an improved YOLO v8n-seg model named YOLO v8n-seg-FCA-BiFPN was proposed for cow body part segmentation tasks. The improved model added FCA channel attention mechanism to the YOLO v8n backbone feature extraction network to better extract the geometric feature information of small targets, and used repeated weighted bidirectional features in the network feature fusion layer. The BiFPN was used to achieve the purpose of increasing the coupling of features at each scale. In order to validate the model performance, side-view images of cows at the channel were collected for network training. To ensure the quality of the dataset, the structural similarity algorithm was used to remove similar redundant images, resulting in a total of 1452 images. LabelMe software was used to label the target cows, which were divided into eight parts, forelimbs, hindlimbs, udders, tails, belly, head, neck, and trunk, and was sent to the training model. The test results showed that the precision was 96.6%, the recall was 94.6% and the mean average precision was 97.1%, the parameters number was 3.3×106, and the detection speed was 6.2f/s. The precision of each part was from 90.3% to 98.2%, and the mean average precision was 96.3%. The YOLO v8n-seg-FCA-BiFPN network could realize accurate segmentation of various parts of dairy cows. Compared with the original YOLO v8n, the precision, recall and mean average precision of YOLO v8n-seg-FCA-BiFPN were 3.2 percentages points, 2.6 percentages points and 3.1 percentages points higher than that of YOLO v8n-seg, respectively. The precision under occlusion was 93.8%, the recall value was 91.67%, and the mean average precision was 93.15%. The volume of the improved model remained unchanged and had strong robustness. Under occlusion, the precision was 93.8%, the recall was 91.67%, and the mean average precision was 93.15%. The overall results showed that the research can provide necessary technical support for precise segmentation of dairy cows'' body parts.