基于无人机航拍图像与深度学习的高效濒危鹿种监测
Efficient Endangered Deer Species Monitoring with UAV Aerial Imagery and Deep Learning
AI总结：
本研究利用无人机航拍图像与YOLO深度学习框架，开发定制算法自动识别阿根廷两地的濒危鹿种，高精度检测沼泽鹿并为潘帕斯鹿监测提供见解，提升了野生动物管理效率。
摘要：
本文探讨了在自然栖息地中使用无人机（UAVs）和深度学习来检测濒危鹿种。由于传统的识别过程需要受过训练的人工劳动，在资源和时间上成本高昂，因此需要更高效的解决方案。利用高分辨率航拍图像，应用先进的计算机视觉技术，实现了对阿根廷布宜诺斯艾利斯两个不同项目中鹿类识别过程的自动化。第一个项目Pantano Project涉及巴拉那三角洲的沼泽鹿，而第二个项目WiMoBo则关注坎波斯德尔图尤国家公园的潘帕斯鹿。使用YOLO框架开发了一种定制算法，并在基于无人机采集图像编制的庞大数据集上进行了训练。研究结果表明，该算法能高精度地有效识别沼泽鹿，并为其在潘帕斯鹿上的适用性提供了初步见解，尽管存在一些已注明的局限性。本研究不仅支持了正在进行的保护工作，还强调了将AI与无人机技术相结合以增强野生动物监测和管理实践的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
