One-stage keypoint detection network for end-to-end cow body measurement

Abstract

Body size measurement plays a crucial role in dairy cow breed selection and milk production. Employing intelligent systems for periodic assessments of body size empowers farmers to gauge the nutritional status of cows. The study introduces an end-to-end intelligent approach for the automatic measurement of cow body size via keypoint detection. Introducing Cow Keypoint-Net (CowK-Net), a one-stage dairy cow keypoint detection network. To improve the interaction of cow features at the channel level, we created the Keypoint Refine Machine (KPRM), designed to balance channel and spatial information through separate pathways effectively. Moreover, we devised an efficient hybrid encoder to interact the information across different scales. This encoder combines Convolutional Neural Network (CNN) based cross-scale fusion with Transformer-based intra-scale interaction, thereby optimizing the keypoint processing and integration. Customizing the loss function to the specific characteristics of the cow dataset ensures effective supervision of the keypoint prediction process. Additionally, we transformed the pixel coordinates of keypoints into three dimensions (3D) space, enabling automated measurement of body size. Field testing on a production farm revealed CowK-Net's accuracy, achieving an impressive 92.8%, surpassing existing keypoint detection methods. Notably, the hybrid encoder matched the accuracy of a Transformer-based encoder while reducing the number of parameters by 18%. Compared to manual measurements, our method demonstrated mean relative errors of 2.8%, 6.7%, 4.1%, and 4.4% for oblique body length, body height, hip height, and chest depth, respectively. The CowK-Net demonstrates its efficacy in measuring cow body size, laying solid foundation for the development of body measurement devices.