Smart Agriculture ›› 2024, Vol. 6 ›› Issue (4): 53-63.doi: 10.12133/j.smartag.SA202310001
• Topic--Technological Innovation and Sustainable Development of Smart Animal Husbandry • Previous Articles Next Articles
ZHANG Yanqi1,2, ZHOU Shuo1,2, ZHANG Ning1,2(), CHAI Xiujuan1,2, SUN Tan1,2
Received:
2023-09-28
Online:
2024-07-30
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corresponding author:
ZHANG Yanqi, ZHOU Shuo, ZHANG Ning, CHAI Xiujuan, SUN Tan. A Regional Farming Pig Counting System Based on Improved Instance Segmentation Algorithm[J]. Smart Agriculture, 2024, 6(4): 53-63.
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URL: https://www.smartag.net.cn/EN/10.12133/j.smartag.SA202310001
Table 2
Performance comparison of different instance segmentation models
网络模型 | AP50 | AP(50:95) | 参数量/MB | 单幅图像平均检测时间/ms |
---|---|---|---|---|
YOLACT | 0.689 | 0.399 | 29.2 | 63 |
PolarMask | 0.742 | 0.422 | 36.3 | 151 |
SOLO | 0.811 | 0.537 | 46.2 | 113 |
Mask R-CNN | 0.900 | 0.644 | 43.7 | 500 |
YOLOv5x | 0.865 | 0.502 | 73.9 | 26 |
YOLOv8x | 0.897 | 0.636 | 68.2 | 44 |
YOLOv8x-CBAM | 0.901 | 0.646 | 125.3 | 109 |
YOLOv8x-Ours | 0.901 | 0.660 | 71.7 | 64 |
Table 3
Prediction results of instance segmentation models on the test set with different thresholds
模型 | 评价指标 | 得分阈值 | |||||||
---|---|---|---|---|---|---|---|---|---|
0.05 | 0.1 | 0.15 | 0.2 | 0.25 | 0.3 | 0.35 | 0.4 | ||
YOLACT | MAE | 7.125 | 7.125 | 7.125 | 7.125 | ||||
RMSE | — | 8.369 | 8.369 | 8.369 | 8.369 | ||||
R 2 | 0.327 | 0.327 | 0.327 | 0.327 | |||||
PolarMask | MAE | 5.512 | 5.578 | 2.853 | 4.157 | ||||
RMSE | — | 7.132 | 6.596 | 3.775 | 5.018 | ||||
R 2 | 0.519 | 0.585 | 0.868 | 0.763 | |||||
SOLO | MAE | 3.637 | 2.913 | 3.141 | 4.085 | ||||
RMSE | — | 4.792 | 3.634 | 4.066 | 4.978 | ||||
R 2 | 0.785 | 0.879 | 0.843 | 0.767 | |||||
Mask R-CNN | MAE | 1.969 | 1.769 | 1.754 | 1.769 | ||||
RMSE | — | 2.649 | 2.421 | 2.366 | 2.376 | ||||
R 2 | 0.932 | 0.943 | 0.946 | 0.945 | |||||
YOLOv8x | MAE | 2.169 | 1.985 | 2.462 | 2.892 | 3.077 | |||
RMSE | 2.826 | 2.715 | 3.153 | 3.526 | 3.713 | — | |||
R 2 | 0.922 | 0.928 | 0.903 | 0.879 | 0.866 | ||||
YOLOv8x -CBAM | MAE | 1.985 | 1.831 | 1.923 | 1.835 | 2.431 | |||
RMSE | 2.720 | 2.434 | 2.508 | 2.440 | 3.103 | — | |||
R 2 | 0.928 | 0.942 | 0.939 | 0.939 | 0.906 | ||||
YOLOv8x -Ours | MAE | 1.969 | 1.798 | 1.727 | 1.799 | 2.316 | |||
RMSE | 2.651 | 2.375 | 2.168 | 2.380 | 3.005 | — | |||
R 2 | 0.931 | 0.945 | 0.949 | 0.944 | 0.938 |
Table 4
Pig cuouting results for different instance segmentation models on the test set
模型 | 准确计数的图像 | 误差小于2头猪的图像 | 误差小于3头猪的图像 | 误差大于2头猪的图像 | ||||
---|---|---|---|---|---|---|---|---|
数量 | 占比/% | 数量 | 占比/% | 数量 | 占比/% | 数量 | 占比/% | |
YOLCAT | 22 | 33.8 | 40 | 61.5 | 52 | 80.0 | 13 | 20.0 |
YOLOv5x | 29 | 44.6 | 48 | 73.8 | 56 | 86.2 | 9 | 13.8 |
PolarMask | 33 | 50.8 | 47 | 72.3 | 56 | 86.2 | 9 | 13.8 |
SOLO | 35 | 53.8 | 50 | 76.9 | 58 | 89.2 | 7 | 10.8 |
YOLOv8x | 35 | 53.8 | 49 | 75.4 | 57 | 87.7 | 8 | 12.3 |
Mask R-CNN | 37 | 56.9 | 49 | 75.4 | 56 | 86.2 | 9 | 13.8 |
YOLOv8x-CBAM | 41 | 63.1 | 51 | 78.5 | 59 | 90.8 | 6 | 9.2 |
YOLOv8x-Ours | 43 | 66.2 | 57 | 87.7 | 61 | 93.8 | 4 | 6.2 |
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