Research on UAV dynamic frame rate adaptation and multi-feature fusion network optimization in intelligent monitoring of animal husbandry
文献类型：期刊论文
| 作者 | Luo, Wei2,3,4; Li, Lin2; Luo, Xinping1,5; Shao, Quanqin6,7; Tang, Ruiyin2,3,4; Liu, Ke2,3,4; Li, Xuqing2,3,4; Liu, Xiaohuang1,5; Wang, Qi8; Ren, Dongyue8 |
| 刊名 | PLOS ONE
 |
| 出版日期 | 2025-09-22 |
| 卷号 | 20期号:9页码:e0331850 |
| DOI | 10.1371/journal.pone.0331850 |
| 产权排序 | 6 |
| 文献子类 | Article |
| 英文摘要 | Precision livestock farming, particularly the collective rearing of animals, remains a pivotal area of focus within agricultural research. However, tracking group-raised animals under conditions of poor lighting, occlusion, and complex outdoor environments continues to pose significant challenges. Due to the intricacies of these conditions, existing methodologies frequently encounter reduced tracking accuracy, decelerated processing rates, and recurrent failures amid occlusion and drift. In response to these challenges, this study introduces SiamCMR, a sophisticated RGB-Thermal (RGBT) object tracking framework tailored for the prolonged observation of group-raised Holstein cows. Constructed upon a dual-stream Siamese network architecture, SiamCMR incorporates innovative feature fusion techniques to deliver robust, real-time tracking capabilities. The framework utilizes a Complementary Coupled Feature Fusion (CCFF) module that merges semi-shared convolutional filters with adaptive sigmoid weighting to efficaciously amalgamate modality-specific features derived from RGB and thermal inputs. To further refine the fusion quality under diverse illumination conditions, we have developed a Multimodal Weight Penalty Module (MWPM), which selectively emphasizes informative channels via batch normalization scaling and feature variance analysis. The framework's resilience to occlusions and drift is enhanced through the integration of reinforcement learning. In experimental evaluations using our proprietary dataset, SiamCMR maintained real-time processing at 135 frames per second (FPS), achieving 81.3% precision (PR) and 58.2% success rate (SR). When compared to the baseline Siamese tracker, SiamFT, which recorded 76.5% PR, 56.2% SR, and 45 FPS, our approach exhibited improvements of 4.8% in PR, 2.0% in SR, and a threefold increase in processing speed, thereby enhancing both tracking accuracy and robustness. Moreover, the system's efficacy has been corroborated through successful implementations on a UAV platform in real-world ranch settings. Results from ablation studies under severe occlusions, light interference, low illumination, and low temperatures validate the effectiveness of the primary components. This research delineates an innovative real-time cattle-tracking solution that augments pasture management by facilitating precise monitoring of cow positions, behaviors, and health, ultimately optimizing feeding strategies and enhancing milk quality and safety. |
| URL标识 | 查看原文 |
| WOS关键词 | OBJECT TRACKING ; ALGORITHM |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:001577437000004 |
| 出版者 | PUBLIC LIBRARY SCIENCE |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/216167]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Wang, Dongliang |
| 作者单位 | 1.China Geol Survey, Comprehens Survey Command Ctr Nat Resources, Beijing, Peoples R China; 2.North China Inst Aerosp Engn, Langfang, Peoples R China; 3.Aerosp Remote Sensing Informat Proc & Applicat Col, Langfang, Peoples R China; 4.Natl Joint Engn Res Ctr Space Remote Sensing Infor, Langfang, Peoples R China; 5.Minist Nat Resources, Key Lab Coupling Proc & Effect Nat Resources Eleme, Beijing, Peoples R China; 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China; 7.Univ Chinese Acad Sci, Beijing, Peoples R China; 8.Hebei Geol Surveying & Mapping Inst, Langfang, Peoples R China |
| 推荐引用方式 GB/T 7714 | Luo, Wei,Li, Lin,Luo, Xinping,et al. Research on UAV dynamic frame rate adaptation and multi-feature fusion network optimization in intelligent monitoring of animal husbandry[J]. PLOS ONE,2025,20(9):e0331850. |
| APA | Luo, Wei.,Li, Lin.,Luo, Xinping.,Shao, Quanqin.,Tang, Ruiyin.,...&Wang, Dongliang.(2025).Research on UAV dynamic frame rate adaptation and multi-feature fusion network optimization in intelligent monitoring of animal husbandry.PLOS ONE,20(9),e0331850. |
| MLA | Luo, Wei,et al."Research on UAV dynamic frame rate adaptation and multi-feature fusion network optimization in intelligent monitoring of animal husbandry".PLOS ONE 20.9(2025):e0331850. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。