Smart Agriculture ›› 2024, Vol. 6 ›› Issue (5): 153-163.doi: 10.12133/j.smartag.SA202406014
• Technology and Method • Previous Articles
NIAN Yue, ZHAO Kaixuan, JI Jiangtao()
Received:
2024-06-14
Online:
2024-09-30
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NIAN Yue, ZHAO Kaixuan, JI Jiangtao. Cow Hoof Slippage Detecting Method Based on Enhanced DeepLabCut Model[J]. Smart Agriculture, 2024, 6(5): 153-163.
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URL: https://www.smartag.net.cn/EN/10.12133/j.smartag.SA202406014
Table 1
Performance comparison of different backbone network models of detecting cow hoof slippage
网络模型 | 训练集误差/pixel | 验证集误差/pixel | 模型大小/MB | 帧率/(f/s) |
---|---|---|---|---|
ResNet-50 | 2.69 | 3.31 | 98.00 | 35.72 |
ResNet-101 | 2.90 | 3.61 | 170.50 | 27.71 |
ResNet-152 | 2.73 | 3.35 | 230.60 | 20.57 |
MobileNet-V2-0.35 | 4.73 | 5.01 | 4.50 | 48.00 |
MobileNet-V2-0.5 | 4.15 | 4.34 | 15.60 | 48.00 |
MobileNet-V2-0.75 | 4.04 | 4.61 | 26.50 | 40.00 |
MobileNet-V2-1.0 | 4.00 | 4.46 | 52.30 | 36.00 |
EfficientNet-b0 | 3.00 | 3.53 | 19.50 | 33.19 |
EfficientNet-b3 | 3.65 | 4.03 | 47.08 | 23.22 |
EfficientNet-b6 | 5.78 | 5.98 | 165.40 | 19.43 |
Table 4
The evaluation results of the cow lameness classification model performance
折 | 准确率% | 精确度 | 召回率 | F 1分数 |
---|---|---|---|---|
1 | 90.00 | 0.890 | 1.000 | 0.930 |
2 | 86.67 | 0.960 | 0.930 | 0.920 |
3 | 89.65 | 0.930 | 0.960 | 0.940 |
4 | 93.10 | 0.960 | 0.920 | 0.960 |
5 | 89.65 | 0.920 | 0.960 | 0.940 |
6 | 86.20 | 0.920 | 0.930 | 0.940 |
7 | 93.10 | 0.960 | 0.960 | 0.960 |
8 | 96.55 | 1.000 | 0.960 | 0.980 |
9 | 93.10 | 1.000 | 0.920 | 0.920 |
10 | 86.20 | 0.890 | 0.950 | 0.920 |
Mean | 90.42 | 0.943 | 0.949 | 0.941 |
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