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Individual Identification Method of Dairy Cows in Cowsheds under Wide Field of View Based on DAM-ResNet
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Yongsheng SI1, 2, Wenke LU1, 2, Kejian WANG1, 2, Yabin MA3, Ming YUAN3, Bin WANG1, 2, Zhencun WANG4
Transactions of the Chinese Society for Agricultural Machinery | 2025, 56(12) : 581 - 590
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Transactions of the Chinese Society for Agricultural Machinery | 2025, 56(12): 581-590
• Agricultural Informatization Engineering •
Individual Identification Method of Dairy Cows in Cowsheds under Wide Field of View Based on DAM-ResNet
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Yongsheng SI1, 2, Wenke LU1, 2, Kejian WANG1, 2, Yabin MA3, Ming YUAN3, Bin WANG1, 2, Zhencun WANG4
Affiliations
  • 1.College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China
  • 2.Key Laboratory of Agricultural Big Data of Hebei Province, Baoding 071001, China
  • 3.Hebei Provincial Station for Livestock Varieties Producing and Spreading, Shijiazhuang 050061, China
  • 4.Tangshan Jincheng Animal Husbandry Co., Ltd., Tangshan 064000, China
Published: 2025-12-25 doi: 10.6041/j.issn.1000-1298.2025.12.053
Outline
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Aiming at the problems such as occlusion and large deformation of cow images in the cowshed under a wide field of view, which cause difficulties in individual identification, a method for individual identification of cows in the cowshed under a wide field of view based on the DAM-ResNet model was proposed. The Mask R-CNN model was used to segment four types of dairy cow parts: the back, the left side of the trunk, the right side of the trunk and the buttocks. Based on the ResNet34 residual network, the second-generation deformable convolution was introduced to enhance the extraction of deformed image features of cow patterns in the segmentation results. Integrating the AFF attention feature fusion module into the residual structure to achieve accurate recognition of images of small target cows at a distance. The fine-grained classification loss function, interchannel loss (MC-Loss), was adopted to improve the recognition accuracy of the model for cows with similar patterns. The multi-part dataset of dairy cows was constructed by using the segmented images of dairy cows, and the DAM-ResNet model was trained. Individual cow recognition tests were conducted on a dataset of 12 864 images of 57 dairy cows. The results showed that the recognition accuracy rates of the back, left trunk, right trunk and buttocks of dairy cows were 96.13%, 96.56%, 96.94% and 93.14%, respectively, which were 2.76, 2.88, 2.92 and 4.25 percentage points higher than those of the original ResNet model. The recognition accuracy rates of the method proposed within the ranges of 10~20 m, 20~30 m and 30~40 m were 97.54%, 90.72% and 82.17%, respectively. The research results can provide technical support for intelligent dairy cow breeding.

cow individual identification  /  wide-field  /  deformable convolution  /  attentional feature fusion  /  fine-grained classification
司永胜, 鲁文柯, 王克俭, 马亚宾, 袁明, 王斌, 王振存. 基于DAM-ResNet的广视野下牛舍奶牛个体识别方法. 农业机械学报, 2025 , 56 (12) : 581 -590 . DOI: 10.6041/j.issn.1000-1298.2025.12.053
Yongsheng SI, Wenke LU, Kejian WANG, Yabin MA, Ming YUAN, Bin WANG, Zhencun WANG. Individual Identification Method of Dairy Cows in Cowsheds under Wide Field of View Based on DAM-ResNet[J]. Transactions of the Chinese Society for Agricultural Machinery, 2025 , 56 (12) : 581 -590 . DOI: 10.6041/j.issn.1000-1298.2025.12.053
Year 2025 volume 56 Issue 12
140
63
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Article Info
doi: 10.6041/j.issn.1000-1298.2025.12.053
  • Receive Date:2024-08-05
  • Online Date:2026-09-30
  • Published:2025-12-25
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  • Received:2024-08-05
  • Revised:2024-09-15
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Affiliations
    1.College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China
    2.Key Laboratory of Agricultural Big Data of Hebei Province, Baoding 071001, China
    3.Hebei Provincial Station for Livestock Varieties Producing and Spreading, Shijiazhuang 050061, China
    4.Tangshan Jincheng Animal Husbandry Co., Ltd., Tangshan 064000, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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