基于融合坐标信息的改进YOLO V4模型识别奶牛面部

为实现奶牛个体的准确识别，基于YOLO V4目标检测网络，提出了一种融合坐标信息的奶牛面部识别模型。首先，采集71头奶牛面部图像数据集，并通过数据增强扩充提高模型的泛化性能。其次，在YOLO V4网络的特征提取层和检测头部分分别引入坐标注意力机制和包含坐标通道的坐标卷积模块，以增强模型对目标位置的敏感性，提高识别精度。试验结果表明，改进的YOLO V4模型能够有效提取奶牛个体面部特征，平均精度均值（mAP）为93.68%，平均帧率为18帧/s，虽然检测速度低于无锚框的CenterNet，但mAP提高了10.92%；与Faster R-CNN和SSD模型相比，在检测速度提高的同时，精度分别提高了1.51%和16.32%；与原始YOLO V4相比，mAP提高0.89%，同时检测速度基本不变。该研究为奶牛精准养殖中的牛脸图像识别提供了一种有效的技术支持。

Improved YOLO V4 model for face recognition of diary cow by fusing coordinate information

Individual identity identification of dairy cows is one of the most prerequisites for the intelligent, precision, and large-scale breeding of dairy cows. It can also provide basic information for the formulation of individual feeding plans, milk production efficiency, and health status analysis. As such, an important link can serve in the management of milk source traceability, disease prevention, and insurance claim settlement. Traditional artificial identification of cows, such as ear tags, brands, neck chains, and pricks, is time-consuming and laborious, particularly easy to cause a stress response, resulting in injuries to cows and people. Current identification using Radio Frequency Identification (RFID) or some physiological characteristics, such as bovine nose mirror lines, iris, retinal blood vessels, still have some defects in durability, cost, and accessibility. In this study, a cow face identification was proposed to fuse with the coordinate information using an improved YOLO V4 detection model, in order to identify individual dairy cows accurately and nondestructively. Holstein cow was also taken as a research object. First, 71 facial images were collected in an actual dairy farm over three years, including the cows with different growth stages, various lighting conditions, postures, and degrees of occlusion. A preprocessing step was also selected to remove the blurry, severe occlusion, insufficient light, and abnormal images. The preprocessed dataset was enhanced and then expanded by -10° to 10° rotation, random brightness adjustment, and cropping, thereby improving the generalization performance of the model. In total, 16614 images of the training set were obtained, including 10940 images in 2019 and 2020, and some 5674 images taken in 2021, where the remaining 649 images in 2021 were used as the test set. Secondly, the coordinate attention and coordinate convolution module (CoordConv) containing the coordinate channel were introduced into the feature extraction layer and detection head part of the YOLO V4 network, particularly for the model sensitivity of target location. Finally, the improved YOLO V4 model was compared with 5 object detection models to verify the effectiveness. The test results showed that the average accuracy of the improved YOLO V4 model was 93.68%. Specifically, the new model was improved by 16.32%, 10.92%, 0.89%, 1.51%, and 0.19%, respectively, compared with SSD, CenterNet, YOLO V4, Faster R-CNN, and CA-YOLO V4 model. The improved YOLO V4 model was slightly lower than the original YOLO V4 model, in terms of detection speed. Furthermore, better recognition performance was achieved for the cows with the face occlusion in the improved YOLO V4 model than others. The recognition rate reached 92.60%, 12.79%, 30.52%, 10.95%, and 7.91% higher than that of SSD, CenterNet, YOLO V4, and Faster R-CNN model, respectively. Nevertheless, it was necessary to enhance the recognition accuracy, when the facial features were not obvious leading by large occlusion area and dark light. Consequently, the experiment demonstrated that the coordinate information greatly contributed to enhancing the position sensitivity of the cow face for a higher recognition accuracy in the improved YOLO V4 model. This finding can provide effective technical support to identify the cow face in precise dairy cow breeding.