基于YOLOv11和SVR的猪只背部姿态与体尺估测

李梓芃; 徐迪红; 黎煊; 李良华; 孙华; 黄江东; 王起繁; 梅书棋; 彭先文

doi:10.13300/j.cnki.hnlkxb.2025.05.015

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基于YOLOv11和SVR的猪只背部姿态与体尺估测

动物科学·动物医学 | 更新时间：2026-11-05

- 基于YOLOv11和SVR的猪只背部姿态与体尺估测
- YOLOv11 and SVR based detection of back posture and estimation of body size in breeding pigs
- 华中农业大学学报 2025年44卷第5期页码：142-151
- 作者机构：
  
  1.湖北省农业科学院畜牧兽医研究所，武汉 430064
  2.华中农业大学工学院/ 农业农村部智慧养殖技术重点实验室，武汉 430070
- 作者简介：
  
  李梓芃，E-mail：419215098@qq.com
  彭先文，E-mail：pxwpal@163.com
- 基金信息：
  
  国家重点研发计划项目(2021YFD1301102);湖北省重点研发计划项目(2022BBA0018);湖北省支持种业高质量发展资金项目(HBZY2023B006-03);湖北省农业科技创新中心创新团队项目(2024-620-000-001-014);国家生猪产业技术体系项目(CARS-35)
- DOI：10.13300/j.cnki.hnlkxb.2025.05.015
  中图分类号： S818;TP391.4
- 收稿：2024-11-26，
  
  纸质出版：2025-09-15
- 稿件说明：
移动端阅览
李梓芃，徐迪红，黎煊，等.基于YOLOv11和SVR的猪只背部姿态与体尺估测［J］.华中农业大学学报，2025，44（5）：142‐151.

LI Zipeng,XU Dihong,LI Xuan,et al.YOLOv11 and SVR based detection of back posture and estimation of body size in breeding pigs[J].Journal of Huazhong Agricultural University,2025,44(05):142-151.
李梓芃，徐迪红，黎煊，等.基于YOLOv11和SVR的猪只背部姿态与体尺估测［J］.华中农业大学学报，2025，44（5）：142‐151. DOI： 10.13300/j.cnki.hnlkxb.2025.05.015.

LI Zipeng,XU Dihong,LI Xuan,et al.YOLOv11 and SVR based detection of back posture and estimation of body size in breeding pigs[J].Journal of Huazhong Agricultural University,2025,44(05):142-151. DOI： 10.13300/j.cnki.hnlkxb.2025.05.015.

摘要

针对人工接触式体尺测量导致的种猪应激反应、较大误差及低效问题，设计一种非接触式猪只图像数据采集平台，并提出一种基于YOLOv11与支持向量回归（support vector regression，SVR）算法的猪只背部姿态检测与体尺估测方法。该方法利用YOLOv11模型进行猪只姿态目标检测，并通过SVR算法处理目标检测结果中的猪只体尺像素信息，进而估算猪只的体尺。结果显示，YOLOv11模型的召回率和平均精确率分别达到94.6%和96.0%，展示了良好的检测鲁棒性；通过SVR算法得到的体长、胸宽、臀宽的估测值与实测值的平均绝对百分比误差分别为2.78%、2.55%和2.88%，说明该算法在体尺测量上的效果较好。以上结果表明，基于YOLOv11与支持向量回归（SVR）算法的猪只背部姿态检测与体尺估测方法具有轻量化、高精确率的特点，可有效减少人为误差和猪只应激反应。

Abstract

A non-contact image data collection platform for pigs was designed to solve the problems of the response to stress， large errors， and low efficiency caused by the manual contact measurement of body size in breeding pigs. A method of detecting back posture and estimating body size in pigs was proposed based on YOLOv11 and support vector regression （SVR） algorithm. The YOLOv11 model was used to detect the back posture in pigs and the SVR algorithm was used to process the information of body size from the results of detection to estimate the body size in pigs. The results showed that the recall and average precision of the YOLOv11 model reached 94.6% and 96.0%， respectively， indicating that the model has a good robustness of detection. The mean absolute percentage errors between the estimated and measured value of body length， chest width， and hip width obtained through the SVR algorithm was 2.78%， 2.55%， and 2.88%， respectively， indicating that the algorithm has high accuracy in measuring the body size. It is indicated that the method of detecting back posture and estimating body size in pigs based on YOLOv11 and SVR algorithm has the characteristics of lightweight and high accuracy， providing an efficient and reliable tool for breeding selection in pig farms while reducing human error and the response to stress in pigs.

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references

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