AMS-YOLO: Asymmetric Multi-Scale Fusion Network for Cannabis Detection in UAV Imagery
文献类型：期刊论文
| 作者 | Li, Xuelin1,2; Yue, Huanyin1,2; Liu, Jianli3,4; Cheng, Aonan4 |
| 刊名 | DRONES
 |
| 出版日期 | 2025-09-06 |
| 卷号 | 9期号:9页码:629 |
| 关键词 | UAV imagery cannabis detection YOLO |
| DOI | 10.3390/drones9090629 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Cannabis is a strictly regulated plant in China, and its illegal cultivation presents significant challenges for social governance. Traditional manual patrol methods suffer from low coverage efficiency, while satellite imagery struggles to identify illicit plantations due to its limited spatial resolution, particularly for sparsely distributed and concealed cultivation. UAV remote sensing technology, with its high resolution and mobility, provides a promising solution for cannabis monitoring. However, existing detection methods still face challenges in terms of accuracy and robustness, particularly due to varying target scales, severe occlusion, and background interference. In this paper, we propose AMS-YOLO, a cannabis detection model tailored for UAV imagery. The model incorporates an asymmetric backbone network to improve texture perception by directing the model's focus towards directional information. Additionally, it features a multi-scale fusion neck structure, incorporating partial convolution mechanisms to effectively improve cannabis detection in small target and complex background scenarios. To evaluate the model's performance, we constructed a cannabis remote sensing dataset consisting of 1972 images. Experimental results show that AMS-YOLO achieves an mAP of 90.7% while maintaining efficient inference speed, outperforming existing state-of-the-art detection algorithms. This method demonstrates strong adaptability and practicality in complex environments, offering robust technical support for monitoring illegal cannabis cultivation. |
| URL标识 | 查看原文 |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001581313300001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217557]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Yue, Huanyin |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; 2.Univ Chinese Acad Sci, Beijing 100101, Peoples R China; 3.Xuchang Univ, Coll Geog & Geomatics, Xuchang 461000, Peoples R China 4.China TopRS Technol Co Ltd, Natl Engn Res Ctr Surveying & Mapping, Beijing 100039, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Li, Xuelin,Yue, Huanyin,Liu, Jianli,et al. AMS-YOLO: Asymmetric Multi-Scale Fusion Network for Cannabis Detection in UAV Imagery[J]. DRONES,2025,9(9):629. |
| APA | Li, Xuelin,Yue, Huanyin,Liu, Jianli,&Cheng, Aonan.(2025).AMS-YOLO: Asymmetric Multi-Scale Fusion Network for Cannabis Detection in UAV Imagery.DRONES,9(9),629. |
| MLA | Li, Xuelin,et al."AMS-YOLO: Asymmetric Multi-Scale Fusion Network for Cannabis Detection in UAV Imagery".DRONES 9.9(2025):629. |
入库方式： OAI收割
来源：地理科学与资源研究所
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