Research on individual recognition of dairy cows based on improved Mask R-CNN

LI Haoyue; CHEN Guifen; PEI Ao

doi:10.7671/j.issn.1001-411X.202003030

Journal of South China Agricultural University > 2020 > 41(6): 161-168. > DOI: 10.7671/j.issn.1001-411X.202003030

LI Haoyue, CHEN Guifen, PEI Ao. Research on individual recognition of dairy cows based on improved Mask R-CNN[J]. Journal of South China Agricultural University, 2020, 41(6): 161-168. DOI: 10.7671/j.issn.1001-411X.202003030

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Research on individual recognition of dairy cows based on improved Mask R-CNN

College of Information Technology, Jilin Agricultural University, Changchun 130118, China

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Received Date: March 28, 2020
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
To propose an individual cow recognition method based on the improved Mask R-CNN algorithm, and solve the problem of low efficiency and strong subjectivity of artificially identifying individual cows in traditional dairy farming.

Method
This method optimizes the feature extraction network structure in Mask R-CNN, adopts ResNet-50 network embedded in SE block as backbone, and selects image channels by weighting strategy to improve feature utilization. For the problem of inaccurate target edge positioning during instance segmentation, a boundary weighted loss function is added to construct a new Mask loss function to improve the accuracy of boundary detection. A total of 3000 cow images are trained, validated and tested.

Result
The improved Mask R-CNN model had an average precision (AP) of 100% and IoU_Mask of 91.34%. Compared with the original Mask R-CNN model, AP increased by 3.28% and IoU_Mask increased by 5.92%.

Conclusion
The proposed method has strong segmentation accuracy and robustness, and can provide a reference for accurate recognition of cow images under complex farming environment.
- Mask R-CNN,
- SENet,
- IoU boundary loss,
- image recognition,
- cow recognition

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References (21)

References

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Research on individual recognition of dairy cows based on improved Mask R-CNN