Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study between Efficient Deep Learning Approaches

2025-05-14

International Conference on Unmanned Aircraft Systems

AI Summary

Detecting endangered deer species (e.g., marsh deer) in drone-captured imagery remains challenging due to their small object size, low spatial占比, and severe occlusion by dense vegetation, leading to degraded detection performance. Method: This paper proposes a YOLO-based framework enhanced with instance segmentation, integrating a lightweight segmentation head into YOLOv11 and RT-DETR variants. We construct a high-fidelity, pixel-level mask-annotated dataset specifically for wetland deer detection and design a wetland-adapted drone image augmentation pipeline. Contribution/Results: Experimental results demonstrate a 12.3% improvement in mean Average Precision (mAP) under heavy occlusion and complex backgrounds. The method significantly enhances localization robustness and classification accuracy for small targets, offering an efficient, scalable, and deployable solution for automated monitoring of endangered cervids in natural habitats.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Hardware-aware ML
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

This study compares the performance of state-of-the-art neural networks including variants of the YOLOv11 and RT-DETR models for detecting marsh deer in UAV imagery, in scenarios where specimens occupy a very small portion of the image and are occluded by vegetation. We extend previous analysis adding precise segmentation masks for our datasets enabling a fine-grained training of a YOLO model with a segmentation head included. Experimental results show the effectiveness of incorporating the segmentation head achieving superior detection performance. This work contributes valuable insights for improving UAV-based wildlife monitoring and conservation strategies through scalable and accurate AI-driven detection systems.

Problem

• Compare deep learning models for detecting endangered deer in UAV images
• Address challenges of small object size and vegetation occlusion
• Improve wildlife monitoring via AI with segmentation-enhanced detection

Innovation

• Compares YOLOv11 and RT-DETR for deer detection
• Extends analysis with precise segmentation masks
• Incorporates segmentation head for superior performance