Open Pose Mask R-CNN Network for Individual Cattle Recognition
Cattle’s individual identification plays a crucial role in effectively managing large farms. To enhance agricultural efficiency, promote the digital transformation of animal husbandry, and improve animal welfare, it is essential to employ advanced identification technologies capable of re...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10268406/ |
| _version_ | 1797655662276116480 |
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| author | Jianping Wang Xueyan Zhang Guohong Gao Yingying Lv Qian Li Zhiyu Li Chengchao Wang Guanglan Chen |
| author_facet | Jianping Wang Xueyan Zhang Guohong Gao Yingying Lv Qian Li Zhiyu Li Chengchao Wang Guanglan Chen |
| author_sort | Jianping Wang |
| collection | DOAJ |
| description | Cattle’s individual identification plays a crucial role in effectively managing large farms. To enhance agricultural efficiency, promote the digital transformation of animal husbandry, and improve animal welfare, it is essential to employ advanced identification technologies capable of real-time monitoring of cattle individual. This paper introduces a novel network called Open Pose Mask R-CNN (OP-Mask R-CNN) for individual cattle identification, which combines Open Pose with the Mask R-CNN network. Three key strategies are presented to improve the identification of individual cattle. First, optimize the number of convolutional layers in the Mask R-CNN backbone network, i.e., ResNet101. Second, introduce an Open Pose-based bovine skeleton feature extraction method. Finally, construct a fusion mechanism that combines the attention module, the convolutional block attention module (CBAM), the open pose module, and the ResNet101. Experimental results demonstrate that our proposed method achieves a 5.6% increase in recognition accuracy and improves recognition speed compared to the original Mask R-CNN model. This work strikes a balance between accuracy and complexity, facilitating the development of a lightweight bovine individual recognition technique. |
| first_indexed | 2024-03-11T17:17:46Z |
| format | Article |
| id | doaj.art-b137f106de6d40a7bc648cfbf0e9a23c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T17:17:46Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b137f106de6d40a7bc648cfbf0e9a23c2023-10-19T23:01:00ZengIEEEIEEE Access2169-35362023-01-011111375211376810.1109/ACCESS.2023.332115210268406Open Pose Mask R-CNN Network for Individual Cattle RecognitionJianping Wang0https://orcid.org/0000-0002-8159-1054Xueyan Zhang1Guohong Gao2https://orcid.org/0000-0001-6923-7178Yingying Lv3Qian Li4Zhiyu Li5Chengchao Wang6Guanglan Chen7School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, ChinaSchool of Information Engineering, Henan Institute of Science and Technology, Xinxiang, ChinaSchool of Information Engineering, Henan Institute of Science and Technology, Xinxiang, ChinaSchool of Information Engineering, Henan Institute of Science and Technology, Xinxiang, ChinaSchool of Information Engineering, Henan Institute of Science and Technology, Xinxiang, ChinaSchool of Information Engineering, Henan Institute of Science and Technology, Xinxiang, ChinaSchool of Information Engineering, Henan Institute of Science and Technology, Xinxiang, ChinaSchool of Information Engineering, Henan Institute of Science and Technology, Xinxiang, ChinaCattle’s individual identification plays a crucial role in effectively managing large farms. To enhance agricultural efficiency, promote the digital transformation of animal husbandry, and improve animal welfare, it is essential to employ advanced identification technologies capable of real-time monitoring of cattle individual. This paper introduces a novel network called Open Pose Mask R-CNN (OP-Mask R-CNN) for individual cattle identification, which combines Open Pose with the Mask R-CNN network. Three key strategies are presented to improve the identification of individual cattle. First, optimize the number of convolutional layers in the Mask R-CNN backbone network, i.e., ResNet101. Second, introduce an Open Pose-based bovine skeleton feature extraction method. Finally, construct a fusion mechanism that combines the attention module, the convolutional block attention module (CBAM), the open pose module, and the ResNet101. Experimental results demonstrate that our proposed method achieves a 5.6% increase in recognition accuracy and improves recognition speed compared to the original Mask R-CNN model. This work strikes a balance between accuracy and complexity, facilitating the development of a lightweight bovine individual recognition technique.https://ieeexplore.ieee.org/document/10268406/Cattle identificationmask R-CNN networkopen poseCBAMOP-Mask R-CNN |
| spellingShingle | Jianping Wang Xueyan Zhang Guohong Gao Yingying Lv Qian Li Zhiyu Li Chengchao Wang Guanglan Chen Open Pose Mask R-CNN Network for Individual Cattle Recognition IEEE Access Cattle identification mask R-CNN network open pose CBAM OP-Mask R-CNN |
| title | Open Pose Mask R-CNN Network for Individual Cattle Recognition |
| title_full | Open Pose Mask R-CNN Network for Individual Cattle Recognition |
| title_fullStr | Open Pose Mask R-CNN Network for Individual Cattle Recognition |
| title_full_unstemmed | Open Pose Mask R-CNN Network for Individual Cattle Recognition |
| title_short | Open Pose Mask R-CNN Network for Individual Cattle Recognition |
| title_sort | open pose mask r cnn network for individual cattle recognition |
| topic | Cattle identification mask R-CNN network open pose CBAM OP-Mask R-CNN |
| url | https://ieeexplore.ieee.org/document/10268406/ |
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