## 4 Conclusions and Discussion  
In this research, we propose a method for measuring the body parameters of Mongolian horses based on deep learning and image processing, and the main conclusions are as follows:  
1. Our method can ensure that the somatic parameters of a Mongolian horse's athletic performance ability are acquired when the horse is in its natural state; in addition, the method proposed in this paper enhanced the ability to obtain the Mongolian horses' somatic measurement parameters, not only acquiring the horses' growth information in a contactless way but also being able to be used in selection and breeding programs for improving the Mongolian horse's athletic performance ability. 2. Utilizing the improved Mask R-CNN model for contour extraction of the captured images of horses in a natural walking state can effectively solve the problem of the occurrence of rough edge contours caused by the original Mask R-CNN model because it has a lower feature space resolution and a smaller proportion of boundary pixels, thus improving the target Mongolian horse contour segmentation accuracy. The model in this paper was compared with the Mask R-CNN model, the YOLACT model, [PERSON] et al. 's model [32], the SOLO model, and [PERSON] et al. 's model [47]: the PA yielded by our model was 7.26, 6.39, 8.57, 5.28, and 0.46 percentage points higher, and the MIoU was 10.64, 9.85, 9.99, 8.90, and 0.39 percentage points higher, respectively. 3. Using the improved Harris-based algorithm, the optimal threshold size was obtained through iteration, and then the feature points in Mongolian horse contour images were detected according to this threshold, effectively solving the problem of the inaccuracy  
Figure 11: Relative error box plot of body measurements. of the detected feature points due to the irrational threshold setting of the original Harris algorithm, and the obtained feature points were used as the location of the measurement points. The contours were divided based on the center of mass of a horse in an image, a fitting operation was performed on the curves in each interval, and the point of maximum curvature in the curves was calculated as the measurement point position. The average relative errors of the linear and angular parameters calculated in connection with the scale parameters are 3.58% and 5.06%, respectively, allowing for a more accurate estimation of body parameters. 4. The method proposed in this paper is computationally intensive and relies on multiple algorithms. While the approach proposed in this paper emphasizes 2D body size measurements, critical values such as withers height, body height, chest depth, body length, hip height, shoulder angle, and hip angle can be effectively gauged using just a single RGB camera. Despite a marginally increased processing time in contrast to that reported in other studies, expenditure was minimized in this study due to the usage of a single RGB camera. Future research will focus on making this algorithm simpler and more integrated. Conceptualization, methodology, writing--original draft preparation, and investigation, L.S. ; validation, formal analysis, and writing--review and editing, M.L. and Z.Z. ; and writing--review and editing, supervision, Y.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by The National Natural Science Foundation of China (32360856), and The Natural Science Foundation of Inner Mongolia Autonomous Region (2022 QN03019), and The Scientific Research Program of Higher Education Institutions in Inner Mongolia Autonomous Region (NJZY22516), and The Innovation Team of Higher Education Institutions in Inner Mongolia Autonomous Region (NMGIRT2312). Ethical review and approval were waived for this study due to the fact that our study did not cause any stress to the horses and there was no contact with the horses during the collection process, which would not have affected the normal activities of the horses anyway.