用无人机和相机陷阱结合深度学习，自动识别野生动物与人类活动重叠区。
Multi-perspective monitoring of wildlife and human activities from camera traps and drones with deep learning models
- 融合可见光、红外相机与热成像无人机数据，构建多视角监测体系。
- YOLOv11s在相机图像中达96.2%精度，热成像用改进Faster RCNN增强检测。
- 发现尼泊尔奇坦万国家公园内人兽活动热点及潜在冲突区域。
野生动物与人类活动是景观系统的关键组成部分，其空间分布特征对评估人兽互动及制定有效保护规划至关重要。本研究在尼泊尔奇坦万国家公园（CNP）及其周边地区，结合可见光/近红外相机陷阱与热红外无人机影像，于2022年2月至7月采集图像，构建训练与测试数据集，采用深度学习模型实现野生动物与人类活动的自动化识别。无人机热成像数据提供了补充的空中视角，用于目标检测。通过空间模式分析，识别出动物与居民活动的热点区域，并划定潜在的人兽冲突区。测试中YOLOv11s表现最优，精度达96.2%，召回率92.3%，mAP50为96.7%，mAP75为81.3%。改进的Faster RCNN模型用于热成像分析，增强了检测能力。结果揭示了自然保护区内人兽活动重叠现象，表明多视角自动化监测可显著提升野生动物监管与景观管理效能。
原文摘要 · Abstract (English)
Wildlife and human activities are key components of landscape systems. Understanding their spatial distribution is essential for evaluating human wildlife interactions and informing effective conservation planning. Multiperspective monitoring of wildlife and human activities by combining camera traps and drone imagery. Capturing the spatial patterns of their distributions, which allows the identification of the overlap of their activity zones and the assessment of the degree of human wildlife conflict. The study was conducted in Chitwan National Park (CNP), Nepal, and adjacent regions. Images collected by visible and nearinfrared camera traps and thermal infrared drones from February to July 2022 were processed to create training and testing datasets, which were used to build deep learning models to automatic identify wildlife and human activities. Drone collected thermal imagery was used for detecting targets to provide a multiple monitoring perspective. Spatial pattern analysis was performed to identify animal and resident activity hotspots and delineation potential human wildlife conflict zones. Among the deep learning models tested, YOLOv11s achieved the highest performance with a precision of 96.2%, recall of 92.3%, mAP50 of 96.7%, and mAP50 of 81.3%, making it the most effective for detecting objects in camera trap imagery. Drone based thermal imagery, analyzed with an enhanced Faster RCNN model, added a complementary aerial viewpoint for camera trap detections. Spatial pattern analysis identified clear hotspots for both wildlife and human activities and their overlapping patterns within certain areas in the CNP and buffer zones indicating potential conflict. This study reveals human wildlife conflicts within the conserved landscape. Integrating multiperspective monitoring with automated object detection enhances wildlife surveillance and landscape management.
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