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

TL;DR

This study demonstrates that UAVs combined with deep learning, specifically YOLO, can effectively automate the detection of endangered deer species in their natural habitats, improving conservation efforts.

Contribution

The paper introduces a tailored YOLO-based algorithm trained on UAV imagery for efficient endangered deer detection in diverse environments.

Findings

High accuracy in marsh deer identification

Potential applicability to Pampas deer with limitations

Supports conservation and wildlife monitoring

Abstract

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.