Automatic and accurate measurement of cattle body based on a lightweight YOLOv8-Pose model and 3D point cloud

In animal husbandry, the transition from traditional manual and time-consuming methods to non-contact body measurements for cattle has become increasingly important. Current research predominantly relies on 3D data to extract body measurement key points; however, the presence of noise in the data often leads to significant errors. Furthermore, the complexity of deep learning models often results in poor real-time performance in practical applications. To address these challenges, this study presents the YOLOv8-Pose model with a lightweight shared convolutional detection head (YOLOv8-Pose-LSCDH), which integrates with 3D point clouds for the automatic measurement of dairy cattle. Initially, this method employs a Kinect V2 sensor to capture RGB and depth images of the cattle. The key points predicted by the YOLOv8-Pose-LSCDH on the RGB images are subsequently projected onto the 3D point cloud generated from the depth image, thereby obtaining three-dimensional body measurement key points. These key points are then used to compute the cattle's body dimensions, including withers height, hip height, chest depth, chest circumference, body length, and hip length. Experimental results demonstrate that, compared to the baseline model, the proposed YOLOv8-Pose-LSCDH model requires fewer parameters and less computational load without compromising accuracy, thus offering superior real-time performance. Additionally, when compared to manual measurement, the proposed body measurement method maintains an overall relative error of <4 %.