A generalizable model for the facial recognition of sika deer with enhanced cross-domain performance
Animal recognition technology is recognized as a fundamental tool for ecological conservation, species monitoring, and scientific research. The population management of sika deer, a first-class protected species in China, heavily depends on precise individual identification. However, challenges such...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525002047 |
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| author | Ye Mu Jinghuan Hu Zhipeng Li Heyang Wang He Gong Yu Sun Tianli Hu |
| author_facet | Ye Mu Jinghuan Hu Zhipeng Li Heyang Wang He Gong Yu Sun Tianli Hu |
| author_sort | Ye Mu |
| collection | DOAJ |
| description | Animal recognition technology is recognized as a fundamental tool for ecological conservation, species monitoring, and scientific research. The population management of sika deer, a first-class protected species in China, heavily depends on precise individual identification. However, challenges such as complex backgrounds, occlusions, and age-related facial variations pose significant obstacles to robust facial recognition. To address these domain-specific limitations, an enhanced YOLOv11-based architecture, DeerFace-YOLO, is proposed. The model is validated through transfer learning across developmental stages using a curated dataset of 40 individuals with diverse postures in complex environments. To assess scalability, the framework is extended to pig facial recognition and cattle individual detection tasks, demonstrating cross-species generalization capabilities. A dual-channel architecture is implemented, integrating Vision Transformer (ViT) and Convolutional Neural Networks (CNNs) to synergize local feature extraction with global contextual modeling, thereby mitigating CNN’s limited receptive fields and ViT’s computational complexity. Background interference is suppressed through adaptive attention mechanisms. For multi-scale feature optimization, a Multi-Scale Feature Enhancement (MSFE) module is designed to dynamically prioritize task-relevant features. Additionally, a Reparameterizable Lightweight Detection Head (RLDH) is developed to maintain accuracy while reducing parameter redundancy by 33 %. Experimental results demonstrate state-of-the-art performance, achieving 98.7 % recognition accuracy and a threefold reduction in computational load compared to baseline models. The framework establishes a standardized dataset for non-invasive animal identification and provides a scalable solution for ecological conservation and precision livestock management. |
| format | Article |
| id | doaj-art-9d9476a3539c4b229c4f3deb4c2a98f5 |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-9d9476a3539c4b229c4f3deb4c2a98f52025-08-20T03:53:01ZengElsevierSmart Agricultural Technology2772-37552025-08-011110097110.1016/j.atech.2025.100971A generalizable model for the facial recognition of sika deer with enhanced cross-domain performanceYe Mu0Jinghuan Hu1Zhipeng Li2Heyang Wang3He Gong4Yu Sun5Tianli Hu6College of Information Technology, Jilin Agricultural University, Changchun 130118, China; Jilin Province Agricultural Internet of Things Technology Collaborative Innovation Center, Changchun 130118, China; Jilin Province Intelligent Environmental Engineering Research Center, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaFaculty of Animal Science and Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, China; Jilin Province Agricultural Internet of Things Technology Collaborative Innovation Center, Changchun 130118, China; Jilin Province Intelligent Environmental Engineering Research Center, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, China; Jilin Province Agricultural Internet of Things Technology Collaborative Innovation Center, Changchun 130118, China; Jilin Province Intelligent Environmental Engineering Research Center, Changchun 130118, China; Corresponding author at: College of Information Technology, Jilin Agricultural University, Changchun 130118, China.Animal recognition technology is recognized as a fundamental tool for ecological conservation, species monitoring, and scientific research. The population management of sika deer, a first-class protected species in China, heavily depends on precise individual identification. However, challenges such as complex backgrounds, occlusions, and age-related facial variations pose significant obstacles to robust facial recognition. To address these domain-specific limitations, an enhanced YOLOv11-based architecture, DeerFace-YOLO, is proposed. The model is validated through transfer learning across developmental stages using a curated dataset of 40 individuals with diverse postures in complex environments. To assess scalability, the framework is extended to pig facial recognition and cattle individual detection tasks, demonstrating cross-species generalization capabilities. A dual-channel architecture is implemented, integrating Vision Transformer (ViT) and Convolutional Neural Networks (CNNs) to synergize local feature extraction with global contextual modeling, thereby mitigating CNN’s limited receptive fields and ViT’s computational complexity. Background interference is suppressed through adaptive attention mechanisms. For multi-scale feature optimization, a Multi-Scale Feature Enhancement (MSFE) module is designed to dynamically prioritize task-relevant features. Additionally, a Reparameterizable Lightweight Detection Head (RLDH) is developed to maintain accuracy while reducing parameter redundancy by 33 %. Experimental results demonstrate state-of-the-art performance, achieving 98.7 % recognition accuracy and a threefold reduction in computational load compared to baseline models. The framework establishes a standardized dataset for non-invasive animal identification and provides a scalable solution for ecological conservation and precision livestock management.http://www.sciencedirect.com/science/article/pii/S2772375525002047Facial recognitionSika deer detectionYOLOv11Transfer learningAnimal recognition |
| spellingShingle | Ye Mu Jinghuan Hu Zhipeng Li Heyang Wang He Gong Yu Sun Tianli Hu A generalizable model for the facial recognition of sika deer with enhanced cross-domain performance Smart Agricultural Technology Facial recognition Sika deer detection YOLOv11 Transfer learning Animal recognition |
| title | A generalizable model for the facial recognition of sika deer with enhanced cross-domain performance |
| title_full | A generalizable model for the facial recognition of sika deer with enhanced cross-domain performance |
| title_fullStr | A generalizable model for the facial recognition of sika deer with enhanced cross-domain performance |
| title_full_unstemmed | A generalizable model for the facial recognition of sika deer with enhanced cross-domain performance |
| title_short | A generalizable model for the facial recognition of sika deer with enhanced cross-domain performance |
| title_sort | generalizable model for the facial recognition of sika deer with enhanced cross domain performance |
| topic | Facial recognition Sika deer detection YOLOv11 Transfer learning Animal recognition |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525002047 |
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