## 1 Introduction
[24] used four scanners to scan horses simultaneously from four directions and combined the images from the four scans into one complete image of each horse. Height at the withers, back, and croup; chest depth; width of the chest, croup, and waist; girth circumference; cannon circumference; and body length were measured. Compared with manual measurement, the relative error range of this system was \(-\)1.89-7.05%. These advances help to measure multiple scale parameters of horses with a single image and enable non-contact measurements. Animal body parameter measurement and behavior recognition using deep learning and machine vision technology has become a research hotspot [25; 26; 27; 28]. Although body measurements based on reflective markers attached to the body require contact with an animal, computer vision and deep learning techniques have evolved to a point where unmarked motion capture is possible [29; 30; 31]. [PERSON] et al. [32] proposed a method for keyframe extraction of the trunk in overhead walking videos of cows excluding the head and neck, using the maximum curvature point of the curve in the trunk as the measurement point to obtain the body parameters of the cows. The accuracy rates of keyframe extraction and head and neck removal were 97.36% and 94.04%, respectively, and the average relative error of the body parameter measurement results was maintained within 3.3%. [PERSON] et al. [23] reconstructed the trunks of cows in three dimensions using Structure from Motion (SfM) and then annotated measurement points based on the morphological features of cows and calculated body parameters based on the positional relationships between measurement points. [PERSON] et al. [33] proposed a semantic segmentation model combining attention modules to segment point clouds of pigs into different parts and measure body parameters for different segmented parts, with measurement errors for various parameterscontrolled within 5 cm. [PERSON] et al. [34] used U-Net to segment goat trunk images from complex background environments and employed the Hourglass key point detection model to locate body dimension measurement points in segmented goat images. The experimental results showed that the average relative errors for shoulder height, hip height, body diagonal length, and chest depth were 0.65%, 1.06%, 1.77%, and 1.99%, respectively. This demonstrates that utilizing CNN-based key point localization models in livestock body dimension measurement tasks can also yield high measurement accuracy. [PERSON] et al. [35] proposed the SimCC-ShuffleNetV2 lightweight key point detection model for cows to address the issue of high network complexity in existing deep learning models. This model has a floating-point operation of 0.15 GB, a parameter quantity of 1.31 \(\times\) 10\({}^{6}\), and a detection speed of 10.87 frames per second, providing technical support for tasks such as cow body dimension measurement, behavior recognition, and weight estimation. Although manual marking can significantly improve the accuracy of subsequent marking work, this method still has the disadvantages of a heavy workload and a low degree of automation. Body measurement based on a single image saves more time than using multi-angle images. In summary, both domestic and international researchers have made significant advancements in employing computer vision technology to measure livestock body parameters in a non-contact manner. Despite these advancements, there remain critical challenges that need to be addressed, particularly in the precise segmentation of horse body parts. The complexity of accurately identifying and segmenting various parts of a horse's body poses a significant obstacle. For Mongolian horses, which are known for their sensitivity and dynamic behavior, existing methods fall short of providing accurate body parameter measurements in a natural, non-contact manner. The evaluation of motor performance and the measurement of body parameters based on the key points of horse anatomy have been proven to be feasible [13].