标题： 利用无人机航拍图像和深度学习监测濒危鹿科物种 显示英文标题

标题： Efficient Endangered Deer Species Monitoring with UAV Aerial Imagery and Deep Learning

摘要： 本文研究了利用无人机（UAV）和深度学习技术来检测濒危鹿种在其自然栖息地中的应用。 由于传统的识别过程需要训练有素的人力，这可能在资源和时间上都较为昂贵，因此亟需更高效的解决方案。 通过利用高分辨率的航拍图像，本文采用了先进的计算机视觉技术，以实现阿根廷布宜诺斯艾利斯两个不同项目中的鹿种自动识别。 第一个项目“潘塔诺计划”涉及巴拉那三角洲的沼泽鹿，而第二个项目“WiMoBo”则专注于图尤国家公园的潘帕斯鹿。 使用YOLO框架并基于从无人机拍摄的图像构建的大规模数据集，开发了一种定制化的算法。 研究结果表明，该算法能够高效准确地识别沼泽鹿，并初步展示了其应用于潘帕斯鹿的可能性，尽管存在一些局限性。 本研究不仅支持了正在进行的保护工作，还强调了将人工智能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。 显示英文摘要

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 Paran\'a Delta, while the second, WiMoBo, focuses on the Pampas deer in Campos del Tuy\'u 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.