用无人机+AI自动识别濒危鹿类，提升保护效率
Efficient Endangered Deer Species Monitoring with UAV Aerial Imagery and Deep Learning
- 基于YOLO框架开发专用算法，利用无人机航拍图训练
- 对沼泽鹿识别准确率高，对潘帕斯鹿初步有效但有局限
- 适合野生动物保护机构与生态监测团队应用
本文研究了利用无人机（UAV）与深度学习技术，在阿根廷布宜诺斯艾利斯地区自然栖息地自动检测濒危鹿类的方法。传统人工识别依赖专业人员，耗时耗力。本研究采用高分辨率航拍图像，结合先进计算机视觉技术，实现对两种鹿类的自动化识别：一是巴拉那三角洲的沼泽鹿（Pantano Project），二是图尤市国家公园的潘帕斯鹿（WiMoBo项目）。基于大量无人机拍摄图像构建的数据集，使用YOLO框架开发了定制化算法。结果表明，该算法对沼泽鹿识别具有高准确率，并对潘帕斯鹿初步展现出适用性，但仍存在局限。本研究不仅支持当前保护工作，也凸显了将人工智能与无人机技术融合在野生动物监测与管理中的巨大潜力。
原文摘要 · Abstract (English)
This paper examines the use of Unmanned Aerial Vehicles (UAVs) and deep learning for detecting endangered deer species in their natural habitats. As traditional identification processes require trained manual labor that can be costly in resources and time, there is a need for more efficient solutions. Leveraging high-resolution aerial imagery, advanced computer vision techniques are applied to automate the identification process of deer across two distinct projects in Buenos Aires, Argentina. The first project, Pantano Project, involves the marsh deer in the Paraná Delta, while the second, WiMoBo, focuses on the Pampas deer in Campos del Tuyú National Park. A tailored algorithm was developed using the YOLO framework, trained on extensive datasets compiled from UAV-captured images. The findings demonstrate that the algorithm effectively identifies marsh deer with a high degree of accuracy and provides initial insights into its applicability to Pampas deer, albeit with noted limitations. This study not only supports ongoing conservation efforts but also highlights the potential of integrating AI with UAV technology to enhance wildlife monitoring and management practices.
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