A Lightweight and High-Accuracy Deep Learning Method for Grassland Grazing Livestock Detection Using UAV Imagery
文献类型：期刊论文
| 作者 | Wang, Yuhang4; Ma, Lingling; Wang, Qi; Wang, Ning; Wang, Dongliang2; Wang, Xinhong; Zheng, Qingchuan3; Hou, Xiaoxin3; Ouyang, Guangzhou |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-03-01 |
| 卷号 | 15期号:6页码:1593 |
| 关键词 | unmanned aerial vehicle (UAV) deep learning object detection grassland grazing livestock remote sensing image |
| DOI | 10.3390/rs15061593 |
| 文献子类 | Article |
| 英文摘要 | Unregulated livestock breeding and grazing can degrade grasslands and damage the ecological environment. The combination of remote sensing and artificial intelligence techniques is a more convenient and powerful means to acquire livestock information in a large area than traditional manual ground investigation. As a mainstream remote sensing platform, unmanned aerial vehicles (UAVs) can obtain high-resolution optical images to detect grazing livestock in grassland. However, grazing livestock objects in UAV images usually occupy very few pixels and tend to gather together, which makes them difficult to detect and count automatically. This paper proposes the GLDM (grazing livestock detection model), a lightweight and high-accuracy deep-learning model, for detecting grazing livestock in UAV images. The enhanced CSPDarknet (ECSP) and weighted aggregate feature re-extraction pyramid modules (WAFR) are constructed to improve the performance based on the YOLOX-nano network scheme. The dataset of different grazing livestock (12,901 instances) for deep learning was made from UAV images in the Hadatu Pasture of Hulunbuir, Inner Mongolia, China. The results show that the proposed method achieves a higher comprehensive detection precision than mainstream object detection models and has an advantage in model size. The mAP of the proposed method is 86.47%, with the model parameter 5.7 M. The average recall and average precision can be above 85% at the same time. The counting accuracy of grazing livestock in the testing dataset, when converted to a unified sheep unit, reached 99%. The scale applicability of the model is also discussed, and the GLDM could perform well with the image resolution varying from 2.5 to 10 cm. The proposed method, the GLDM, was better for detecting grassland grazing livestock in UAV images, combining remote sensing, AI, and grassland ecological applications with broad application prospects. |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:000959618900001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200817]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Inner Mongolia North Heavy Ind Grp Co Ltd, Baotou 014033, Peoples R China 2.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 4.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Quantitat Remote Sensing Informat Technol, Beijing 100094, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Yuhang,Ma, Lingling,Wang, Qi,et al. A Lightweight and High-Accuracy Deep Learning Method for Grassland Grazing Livestock Detection Using UAV Imagery[J]. REMOTE SENSING,2023,15(6):1593. |
| APA | Wang, Yuhang.,Ma, Lingling.,Wang, Qi.,Wang, Ning.,Wang, Dongliang.,...&Ouyang, Guangzhou.(2023).A Lightweight and High-Accuracy Deep Learning Method for Grassland Grazing Livestock Detection Using UAV Imagery.REMOTE SENSING,15(6),1593. |
| MLA | Wang, Yuhang,et al."A Lightweight and High-Accuracy Deep Learning Method for Grassland Grazing Livestock Detection Using UAV Imagery".REMOTE SENSING 15.6(2023):1593. |
入库方式： OAI收割
来源：地理科学与资源研究所
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