An Efficient Algorithm for Small Livestock Object Detection in Unmanned Aerial Vehicle Imagery
文献类型：期刊论文
| 作者 | Chen, Wenbo1,2,3,4; Wang, Dongliang1; Xie, Xiaowei2,3,4 |
| 刊名 | ANIMALS
 |
| 出版日期 | 2025-06-18 |
| 卷号 | 15期号:12页码:1794 |
| 关键词 | unmanned aerial vehicle (UAV) deep learning object detection livestock population surveys |
| ISSN号 | 2076-2615 |
| DOI | 10.3390/ani15121794 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Livestock population surveys are crucial for grassland management tasks such as health and epidemic prevention, grazing prohibition, rest grazing, and forage-livestock balance assessment. These tasks are integral to the modernization and upgrading of the livestock industry and the sustainable development of grasslands. Unmanned aerial vehicles (UAVs) provide significant advantages in flexibility and maneuverability, making them ideal for livestock population surveys. However, grazing livestock in UAV images often appear small and densely packed, leading to identification errors. To address this challenge, we propose an efficient Livestock Network (LSNET) algorithm, a novel YOLOv7-based network. Our approach incorporates a low-level prediction head (P2) to detect small objects from shallow feature maps, while removing a deep-level prediction head (P5) to mitigate the effects of excessive down-sampling. To capture high-level semantic features, we introduce the Large Kernel Attentions Spatial Pyramid Pooling (LKASPP) module. In addition, we replaced the original CIoU with the WIoU v3 loss function. Furthermore, we developed a dataset of grazing livestock for deep learning using UAV images from the Prairie Chenbarhu Banner in Hulunbuir, Inner Mongolia. Our results demonstrate that the proposed module significantly improves the detection accuracy for small livestock objects, with the mean Average Precision (mAP) increasing by 1.47% compared to YOLOv7. Thus, this work offers a novel and practical solution for livestock detection in expansive farms. It overcomes the limitations of existing methods and contributes to more effective livestock management and advancements in agricultural technology. |
| URL标识 | 查看原文 |
| WOS研究方向 | Agriculture ; Veterinary Sciences ; Zoology |
| 语种 | 英语 |
| WOS记录号 | WOS:001515143000001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/214611]  |
| 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.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China; 2.East China Univ Technol, Sch Surveying & Geoinformat Engn, Nanchang 330013, Peoples R China; 3.East China Univ Technol, Jiangxi Key Lab Watershed Ecol Proc & Informat, Platform 2023SSY01051, Nanchang 330013, Peoples R China; 4.East China Univ Technol, Key Lab Mine Environm Monitoring & Improving Poyan, Minist Nat Resources, Nanchang 330013, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Wenbo,Wang, Dongliang,Xie, Xiaowei. An Efficient Algorithm for Small Livestock Object Detection in Unmanned Aerial Vehicle Imagery[J]. ANIMALS,2025,15(12):1794. |
| APA | Chen, Wenbo,Wang, Dongliang,&Xie, Xiaowei.(2025).An Efficient Algorithm for Small Livestock Object Detection in Unmanned Aerial Vehicle Imagery.ANIMALS,15(12),1794. |
| MLA | Chen, Wenbo,et al."An Efficient Algorithm for Small Livestock Object Detection in Unmanned Aerial Vehicle Imagery".ANIMALS 15.12(2025):1794. |
入库方式： OAI收割
来源：地理科学与资源研究所
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