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Lightweight individual cow identification based on Ghost combined with attention mechanism
Lili Fu1, Shijun Li2, Shuolin Kong1
1College of Information Technology, Jilin Agricultural University, Changchun, China.
Plos One
|October 6, 2022
Summary
This study introduces an improved ResNet50 model for non-contact cow identification in smart dairy farms. The enhanced model achieves 98.58% accuracy, significantly reducing parameters and size for efficient farm management.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Animal Science
Background:
- Individual cow identification is crucial for intelligent dairy farming.
- Computer vision offers non-contact and practical solutions for animal recognition.
Purpose of the Study:
- To develop an improved ResNet50 model for accurate, non-contact individual cow recognition.
- To enhance feature extraction and reduce model complexity for dairy management.
Main Methods:
- A novel method combining Ghost module and attention mechanism (CBAM) was integrated into ResNet50.
- Cavity convolution was used for coarse-grained feature extraction.
- The model was trained and validated using side-view images of 13 cows.
Main Results:
- The proposed model achieved a recognition accuracy of 98.58%, outperforming the original ResNet50 by 4.8%.
- Model parameters were reduced by 24.85 times, resulting in a model size of only 3.61 MB.
- The model demonstrated good robustness compared to other networks.
Conclusions:
- The developed computer vision approach offers an efficient and accurate solution for individual cow identification.
- This method overcomes limitations of traditional feature extraction, providing a foundation for advanced animal recognition systems.
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