An attentional residual feature fusion mechanism for sheep face recognition
Abstract In the era of globalization and digitization of livestock markets, sheep are considered an essential source of food production worldwide. However, sheep behavior monitoring, disease prevention, and precise management pose urgent challenges in the development of smart ranches. To address the...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-43580-2 |
| _version_ | 1797453009389617152 |
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| author | Yue Pang Wenbo Yu Yongan Zhang Chuanzhong Xuan Pei Wu |
| author_facet | Yue Pang Wenbo Yu Yongan Zhang Chuanzhong Xuan Pei Wu |
| author_sort | Yue Pang |
| collection | DOAJ |
| description | Abstract In the era of globalization and digitization of livestock markets, sheep are considered an essential source of food production worldwide. However, sheep behavior monitoring, disease prevention, and precise management pose urgent challenges in the development of smart ranches. To address these problems, individual identification of sheep has become an increasingly viable solution. Despite the benefits of traditional sheep individual identification methods, such as accurate tracking and record-keeping, they are labor-intensive and inefficient. Popular convolutional neural networks (CNNs) are unable to extract features for specific problems, further complicating the issue. To overcome these limitations, an Attention Residual Module (ARM) is proposed to aggregate the feature mapping between different layers of the CNN. This approach enables the general model of the CNN to be more adaptable to task-specific feature extraction. Additionally, a targeted sheep face recognition dataset containing 4490 images of 38 individual sheep has been constructed. Furthermore, the experimental data was expanded using image enhancement techniques such as rotation and panning. The results of the experiments indicate that the accuracy of the VGG16, GoogLeNet, and ResNet50 networks with the ARM improved by 10.2%, 6.65%, and 4.38%, respectively, compared to these recognition networks without the ARM. Therefore, the proposed method for specific sheep face recognition tasks has been proven effective. |
| first_indexed | 2024-03-09T15:16:43Z |
| format | Article |
| id | doaj.art-8fef5c15837843ed8abd027c8501074d |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:16:43Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-8fef5c15837843ed8abd027c8501074d2023-11-26T13:02:32ZengNature PortfolioScientific Reports2045-23222023-10-0113111110.1038/s41598-023-43580-2An attentional residual feature fusion mechanism for sheep face recognitionYue Pang0Wenbo Yu1Yongan Zhang2Chuanzhong Xuan3Pei Wu4College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural UniversityCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural UniversityCollege of Computer and Information Engineering, Inner Mongolia Agricultural UniversityCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural UniversityCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural UniversityAbstract In the era of globalization and digitization of livestock markets, sheep are considered an essential source of food production worldwide. However, sheep behavior monitoring, disease prevention, and precise management pose urgent challenges in the development of smart ranches. To address these problems, individual identification of sheep has become an increasingly viable solution. Despite the benefits of traditional sheep individual identification methods, such as accurate tracking and record-keeping, they are labor-intensive and inefficient. Popular convolutional neural networks (CNNs) are unable to extract features for specific problems, further complicating the issue. To overcome these limitations, an Attention Residual Module (ARM) is proposed to aggregate the feature mapping between different layers of the CNN. This approach enables the general model of the CNN to be more adaptable to task-specific feature extraction. Additionally, a targeted sheep face recognition dataset containing 4490 images of 38 individual sheep has been constructed. Furthermore, the experimental data was expanded using image enhancement techniques such as rotation and panning. The results of the experiments indicate that the accuracy of the VGG16, GoogLeNet, and ResNet50 networks with the ARM improved by 10.2%, 6.65%, and 4.38%, respectively, compared to these recognition networks without the ARM. Therefore, the proposed method for specific sheep face recognition tasks has been proven effective.https://doi.org/10.1038/s41598-023-43580-2 |
| spellingShingle | Yue Pang Wenbo Yu Yongan Zhang Chuanzhong Xuan Pei Wu An attentional residual feature fusion mechanism for sheep face recognition Scientific Reports |
| title | An attentional residual feature fusion mechanism for sheep face recognition |
| title_full | An attentional residual feature fusion mechanism for sheep face recognition |
| title_fullStr | An attentional residual feature fusion mechanism for sheep face recognition |
| title_full_unstemmed | An attentional residual feature fusion mechanism for sheep face recognition |
| title_short | An attentional residual feature fusion mechanism for sheep face recognition |
| title_sort | attentional residual feature fusion mechanism for sheep face recognition |
| url | https://doi.org/10.1038/s41598-023-43580-2 |
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