Reliable unmanned aerial vehicle-based thermal infrared target detection method for monitoring Procapra przewalskii in Qinghai
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 U...
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| Language: | English |
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Elsevier
2025-12-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125002183 |
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| author | Guoqing Zhang Wei Luo Yongxiang Zhao Quanqin Shao Lin Li Keyu Mei Guohong Li |
| author_facet | Guoqing Zhang Wei Luo Yongxiang Zhao Quanqin Shao Lin Li Keyu Mei Guohong Li |
| author_sort | Guoqing Zhang |
| collection | DOAJ |
| description | 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 background-suppressed 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. |
| format | Article |
| id | doaj-art-e8334aed97a54e14bb9f82c6b0c9e864 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-e8334aed97a54e14bb9f82c6b0c9e8642025-08-20T05:05:05ZengElsevierEcological Informatics1574-95412025-12-019010320910.1016/j.ecoinf.2025.103209Reliable unmanned aerial vehicle-based thermal infrared target detection method for monitoring Procapra przewalskii in QinghaiGuoqing Zhang0Wei Luo1Yongxiang Zhao2Quanqin Shao3Lin Li4Keyu Mei5Guohong Li6North China Institute of Aerospace Engineering, Langfang 065000, China; National & Regional Joint Engineering Research Center for Aerospace Remote Sensing Application Technology, Langfang 065000, China; Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China; Hebei Aerospace Remote Sensing Information Technology Innovation Center, Langfang 065000, ChinaNorth China Institute of Aerospace Engineering, Langfang 065000, China; National & Regional Joint Engineering Research Center for Aerospace Remote Sensing Application Technology, Langfang 065000, China; Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China; Hebei Aerospace Remote Sensing Information Technology Innovation Center, Langfang 065000, China; Corresponding author at: North China Institute of Aerospace Engineering, Langfang 065000, China.North China Institute of Aerospace Engineering, Langfang 065000, China; National & Regional Joint Engineering Research Center for Aerospace Remote Sensing Application Technology, Langfang 065000, China; Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China; Hebei Aerospace Remote Sensing Information Technology Innovation Center, Langfang 065000, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaNorth China Institute of Aerospace Engineering, Langfang 065000, China; National & Regional Joint Engineering Research Center for Aerospace Remote Sensing Application Technology, Langfang 065000, China; Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China; Hebei Aerospace Remote Sensing Information Technology Innovation Center, Langfang 065000, ChinaNorth China Institute of Aerospace Engineering, Langfang 065000, ChinaNorth China Institute of Aerospace Engineering, Langfang 065000, China; National & Regional Joint Engineering Research Center for Aerospace Remote Sensing Application Technology, Langfang 065000, China; Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province, Langfang 065000, China; Hebei Aerospace Remote Sensing Information Technology Innovation Center, Langfang 065000, ChinaProcapra 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 background-suppressed 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.http://www.sciencedirect.com/science/article/pii/S1574954125002183Procapra przewalskii monitoringUAVThermal infrared imageYOLOv8sMulti-frame processing |
| spellingShingle | Guoqing Zhang Wei Luo Yongxiang Zhao Quanqin Shao Lin Li Keyu Mei Guohong Li Reliable unmanned aerial vehicle-based thermal infrared target detection method for monitoring Procapra przewalskii in Qinghai Ecological Informatics Procapra przewalskii monitoring UAV Thermal infrared image YOLOv8s Multi-frame processing |
| title | Reliable unmanned aerial vehicle-based thermal infrared target detection method for monitoring Procapra przewalskii in Qinghai |
| title_full | Reliable unmanned aerial vehicle-based thermal infrared target detection method for monitoring Procapra przewalskii in Qinghai |
| title_fullStr | Reliable unmanned aerial vehicle-based thermal infrared target detection method for monitoring Procapra przewalskii in Qinghai |
| title_full_unstemmed | Reliable unmanned aerial vehicle-based thermal infrared target detection method for monitoring Procapra przewalskii in Qinghai |
| title_short | Reliable unmanned aerial vehicle-based thermal infrared target detection method for monitoring Procapra przewalskii in Qinghai |
| title_sort | reliable unmanned aerial vehicle based thermal infrared target detection method for monitoring procapra przewalskii in qinghai |
| topic | Procapra przewalskii monitoring UAV Thermal infrared image YOLOv8s Multi-frame processing |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125002183 |
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