A Lightweight Automatic Cattle Body Measurement Method Based on Keypoint Detection

Body measurement plays a crucial role in cattle breeding selection. Traditional manual measurement of cattle body size is both time-consuming and labor-intensive. Current automatic body measurement methods require expensive equipment, involve complex operations, and impose high computational costs, which hinder efficient measurement and broad application. To overcome these limitations, this study proposes an efficient automatic method for cattle body measurement. Lateral and dorsal image datasets were constructed by capturing cattle keypoints characterized by symmetry and relatively fixed positions. A lightweight SCW-YOLO keypoint detection model was designed to identify keypoints in both lateral and dorsal cattle images. Building on the detected keypoints, 11 body measurements—including body height, chest depth, abdominal depth, chest width, abdominal width, sacral height, croup length, diagonal body length, cannon circumference, chest girth, and abdominal girth—were computed automatically using established formulas. Experiments were performed on lateral and dorsal datasets from 61 cattle. The results demonstrated that the proposed method achieved an average relative error of 4.7%. Compared with the original model, the parameter count decreased by 58.2%, compute cost dropped by 68.8%, and model size was reduced by 57%, thus significantly improving lightweight efficiency while preserving acceptable accuracy.