Dairy cow body size measurement method based on binocular stereo matching and improved YOLOv8n-Pose keypoint detection

Objective To realize accurate measurement of dairy cow body size, and preicisely assess dairy cow body shape.

Method Addressing the challenges of limited accuracy and low automation in measuring dairy cow body size, a body size measurement method based on binocula stereo matching and improved YOLOv8n-Pose was proposed. The deep learning-based CREStereo was applied for stereo matching to obtain depth information. In YOLOv8n-Pose, the SimAM attention mechanism was introduced to focus more on individual dairy cow identification and key point information. Additionally, the CoordConv was employed to enhance the network’s spatial coordinate perception capability.

Result The improved YOLOv8n-Pose achieved rapid and accurate detection of body size measurement key points for dairy cows. It attained a precision of 94.3%, with model parameters totaling 2.99 M and floating-point operations amounting to 8.40 G. The detection speed reached 55.6 frames/s. By combining stereo matching and improved YOLOv8n-Pose, the maximum average relative error in body size measurement was reduced to 4.19%.

Conclusion The body size measurement method proposed in this paper achieves high accuracy and rapid detection speed, which can meet the practical requirements of body size measurement.