基于关键点检测和多目标跟踪的猪只体尺估计

姚裔芃; 徐晨; 陈鸿基; 刘勇; 徐顺来

doi:10.7671/j.issn.1001-411X.202404012

基于关键点检测和多目标跟踪的猪只体尺估计

姚裔芃^{1, 2,},
徐晨^{1, 2},
陈鸿基²,
刘勇²,
徐顺来^2, ,

1.
西南大学计算机与信息科学学院/软件学院, 重庆 400700
2.
重庆市畜牧科学院/国家生猪技术创新中心, 重庆 402460

基金项目: 重庆市技术创新与应用发展专项重点项目(cstc2021jscx-dxwtBX0008)；国家生猪技术创新中心先导科技项目(NCTIP-XD/B10)

详细信息

作者简介:
姚裔芃，硕士研究生，主要从事计算机视觉研究，E-mail: 499622418@qq.com

通讯作者:
徐顺来，研究员，硕士，主要从事人工智能、机器视觉、物联网与养殖大模型研究，E-mail: 173894636@qq.com

中图分类号: S828；TP391；TP183
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出版历程
- 收稿日期: 2024-04-05
- 网络出版日期: 2024-06-23
- 发布日期: 2024-06-30
- 刊出日期: 2024-08-07

Estimation of pig body measurements based on keypoint detection and multi-object tracking

YAO Yipeng^{1, 2,},
XU Chen^{1, 2},
CHEN Hongji²,
LIU Yong²,
XU Shunlai^2, ,

1.
College of Computer and Information Science/College of Software, Southwest University, Chongqing 400700, China
2.
Chongqing Academy of Animal Science/National Center of Technology Innovation for Pigs, Chongqing 402460, China

摘要

摘要:
目的
减少猪场人工测量猪只体尺的工作量，提高测量精度和工作效率。
方法
本研究提出基于关键点检测和多目标跟踪的猪只体尺自动估计方法，该方法使用Yolov8-Pose模型识别各猪只关键点和目标检测框，利用ByteTrack算法对猪群实时跟踪，引入感兴趣区域规避图像畸变，提高识别速度，同时设计姿态和异常检测过滤算法减少因运动模糊、姿态不正等因素造成的误差。
结果
5个猪栏中24头猪只体长、肩宽、臀宽的平均绝对误差均小于3 cm，平均绝对百分比误差分别维持在4%、6%和7%以内。数据处理速度提升为19.3帧/s。
结论
本研究提出的基于关键点检测和多目标跟踪的猪只体尺估计方法为猪场生产场景提供了一个轻量化、易部署的自动体尺测量解决方案。
- Yolov8-Pose /
- 感兴趣区域 /
- 体尺估计 /
- 关键点 /
- 目标检测框 /
- 猪
Abstract:
Objective
To reduce the manual workload of measuring pig body measurements in pig farms and improve measurement accuracy and efficiency.
Method
An automatic pig body measurement estimation method based on keypoint detection and multi-object tracking was proposed. The method utilized Yolov8-Pose model to identify keypoints and bounding boxes of individual pigs. ByteTrack algorithm was employed for real-time tracking of the pig herd. Regions of interest were introduced to mitigate image distortion and improve recognition speed. Additionally, a posture and anomaly detection filter algorithm was designed to reduce errors caused by motion blur, posture abnormality and other factors.
Result
The mean absolute errors of the body length, shoulder width, and hip width of 24 pigs in five pigpens were less than 3 cm, the mean absolute percentage errors were maintained below 4%, 6% and 7% respectively. The data processing speed reached 19.3 frames/s.
Conclusion
The proposed method for pig body measurement estimation based on keypoint detection and multi-object tracking provides a lightweight and easily deployable solution for automatic body measurement in pig farming scenarios.
- Yolov8-Pose /
- Region of interest /
- Body measurement estimation /
- Keypoint /
- Target detection box /
- Pig

HTML全文

图 1 4个不同场景猪只6个关键点的标注

Figure 1. Annotation of six key points of pigs in four different scenarios

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图 2 经过Mosaic增强后的样本图片

Figure 2. The image enhanced with Mosaic

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图 3 算法的整体流程图

Figure 3. Overall workflow of the algorithm

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图 4 畸变矫正后的图像

Figure 4. The image after distortion correction

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图 5 猪只6个关键点和体尺测量示意图

Figure 5. Schematic diagram of six key points and body measurements for pigs

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图 6 ROI筛选区域

Figure 6. ROI selection area

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图 7 异常猪只姿态过滤示例

Figure 7. Example of abnormal pig postures for filtering

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图 8 目标框(A)和关键点(B)的P-R曲线

Figure 8. P-R curves for bounding boxes (A) and key points (B)

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图 9 随机4张图像的推理效果展示

Figure 9. Demonstration of inference results in four random images

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表 1 3种多目标追踪算法对猪只追踪的验证结果¹⁾

Table 1 Validation results of three multiple target tracking algorithms for pig tracking

场景 Scene	猪数量 Number of pigs	有无遮挡 Occlusion situation	FairMOT			ByteTrack			BoT-SORT
场景 Scene	猪数量 Number of pigs	有无遮挡 Occlusion situation	MOTA/%	IDF1/%	FPS/(帧·s⁻¹)	MOTA/%	IDF1/%	FPS/(帧·s⁻¹)	MOTA/%	IDF1/%	FPS/(帧·s⁻¹)
1	5	无	86.1	87.7	43	91.3	92.9	142	96.7	90.7	59
2	5	无	84.6	85.4	42	94.7	97.3	142	95.3	97.6	58
3	5	有	73.7	72.3	43	67.6	82.0	144	82.8	88.3	59
4	>20	无	76.3	74.9	36	79.8	86.9	129	84.5	91.4	52
1) MOTA：多目标跟踪准确率，IDF1：IDF1分数，FPS：每秒处理帧数　1) MOTA: Multiple object tracking accuracy, IDF1: Identity F1 score, FPS: Frames per second

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表 2 猪只体长、肩宽、臀宽数据偏差对比¹⁾

Table 2 Variance comparison of pig body length, shoulder width and hip width data

性状 Trait	圈栏号(猪只数量) Circle ID (number of pigs)	第1组 Group 1			第2组 Group 2			第3组 Group 3
性状 Trait	圈栏号(猪只数量) Circle ID (number of pigs)	R²	MAE/cm	MAPE/%	R²	MAE/cm	MAPE/%	R²	MAE/cm	MAPE/%
体长 Body length	1(7)	0.83	4.71	5.99	0.89	4.00	4.95	0.50	8.14	9.97
	2(6)	0.92	2.75	3.84	0.93	2.50	3.48	0.45	6.50	8.65
	3(5)	0.67	2.00	2.26	0.82	1.40	1.50	−1.65	5.80	6.34
	4(2)	0.20	12.00	13.98	0.81	6.50	7.10	0.92	4.00	4.15
	5(4)	0.00	2.50	3.66	0.89	1.00	1.47	−2.13	5.25	7.65
	总计 Total(24)	0.85	3.96	5.08	0.94	2.88	3.61	0.75	6.13	7.74
肩宽 Shoulder width	1(7)	0.81	1.57	6.15	0.83	1.71	7.50	−0.54	4.71	18.82
	2(6)	0.61	1.00	5.39	0.81	0.83	4.57	0.04	1.50	7.89
	3(5)	0.71	1.00	4.54	0.77	1.00	4.83	0.58	1.40	6.93
	4(2)	−0.06	4.00	14.59	0.19	3.00	10.24	−1.31	6.00	22.16
	5(4)	−1.13	2.25	13.77	0.28	3.50	12.00	−5.05	3.75	21.87
	总计 Total(24)	0.77	1.63	7.60	0.86	1.25	5.65	0.13	3.17	14.40
臀宽 Hip width	1(7)	0.71	1.86	8.76	0.74	1.86	8.79	−0.33	4.14	18.50
	2(6)	0.72	0.67	3.76	0.81	0.67	4.30	0.67	0.83	5.40
	3(5)	0.71	1.60	7.45	0.84	1.40	6.65	0.27	2.80	13.99
	4(2)	0.27	3.00	11.99	0.59	2.00	7.45	−1.49	5.50	22.60
	5(4)	0.25	1.25	8.06	0.80	0.75	5.12	−4.36	4.25	27.14
	总计 Total(24)	0.81	1.54	7.80	0.88	1.29	6.50	0.25	3.13	15.63
1)R²：决定系数，MAE：平均绝对误差，MAPE：平均绝对百分比误差　1) R²: Determination coefficient, MAE: Mean absolute error, MAPE: Mean absolute percentage error

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