Reliable unmanned aerial vehicle-based thermal infrared target detection method for monitoring Procapra przewalskii in Qinghai
文献类型：期刊论文
| 作者 | Zhang, Guoqing1,2,3,4; Luo, Wei1,2,3,4; Zhao, Yongxiang1,2,3,4; Shao, Quanqin5; Li, Lin1,2,3,4; Mei, Keyu1; Li, Guohong1,2,3,4 |
| 刊名 | ECOLOGICAL INFORMATICS
 |
| 出版日期 | 2025-12-01 |
| 卷号 | 90页码:103209 |
| 关键词 | Procapra przewalskii monitoring UAV Thermal infrared image YOLOv8s Multi-frame processing |
| ISSN号 | 1574-9541 |
| DOI | 10.1016/j.ecoinf.2025.103209 |
| 产权排序 | 5 |
| 文献子类 | Article |
| 英文摘要 | Procapra przewalskii plays a vital role in maintaining ecological balance; however, it faces considerable threats due to habitat degradation and illegal poaching. Monitoring this species using unmanned aerial vehicles (UAVs) has proven to be an effective conservation strategy. A major challenge in UAV-based surveillance of Procapra przewalskii is conducting observations at night or under conditions of poor visible light. To address this issue, this paper presents a thermal infrared (TIR) target monitoring technique using UAVs. This technique employs YOLOv8s as the base model and proposes a multi-frame processing (MFP) method (YOLO-MFP). This method uses the current frame as the primary input and combines optical flow-processed images and backgroundsuppressed images as auxiliary inputs. Background-suppressed images can effectively minimize most background pixels, while regions with high vector values in optical flow-processed images indicate object positions. The model extracts raw feature data, object details, and movement information from these inputs to improve detection performance. Additionally, a small target detection layer is added to reduce missed detections of smaller targets in TIR images while enhancing the overall detection accuracy. Furthermore, the VoVGSCSP module refines the model's neck architecture by effectively merging the feature maps across various stages, reducing computational demands without sacrificing detection precision. Finally, through numerous comparative experiments on our proposed TIR-Procapra przewalskii dataset, YOLO-MFP reaches a mean average precision (mAP@0.5) value of 96.4 %, precision value of 92.6 %, and recall of 97.0 %, making it superior to the current state-of-the-art models. The importance of this study lies in its enhanced monitoring capabilities for Procapra przewalskii, providing valuable insights for future UAV-based wildlife observation efforts. |
| URL标识 | 查看原文 |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001506793500001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/214501]  |
| 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 | Luo, Wei |
| 作者单位 | 1.North China Inst Aerosp Engn, Langfang 065000, Peoples R China; 2.Natl & Reg Joint Engn Res Ctr Aerosp Remote Sensin, Langfang 065000, Peoples R China; 3.Aerosp Remote Sensing Informat Proc & Applicat Col, Langfang 065000, Peoples R China; 4.Hebei Aerosp Remote Sensing Informat Technol Innov, Langfang 065000, Peoples R China; 5.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Guoqing,Luo, Wei,Zhao, Yongxiang,et al. Reliable unmanned aerial vehicle-based thermal infrared target detection method for monitoring Procapra przewalskii in Qinghai[J]. ECOLOGICAL INFORMATICS,2025,90:103209. |
| APA | Zhang, Guoqing.,Luo, Wei.,Zhao, Yongxiang.,Shao, Quanqin.,Li, Lin.,...&Li, Guohong.(2025).Reliable unmanned aerial vehicle-based thermal infrared target detection method for monitoring Procapra przewalskii in Qinghai.ECOLOGICAL INFORMATICS,90,103209. |
| MLA | Zhang, Guoqing,et al."Reliable unmanned aerial vehicle-based thermal infrared target detection method for monitoring Procapra przewalskii in Qinghai".ECOLOGICAL INFORMATICS 90(2025):103209. |
入库方式： OAI收割
来源：地理科学与资源研究所
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