Hierarchical Dual-Model Detection Framework for Spotted Seals Using Deep Learning on UAVs

Abstract

This study introduces a hierarchical dual-model detection framework for accurately monitoring spotted seals (Phoca largha) in the Liaohe River estuary by using deep learning on Unmanned Aerial Vehicles (UAVs). To address challenges such as weak target features, background interference and limited edge computing capacity, this study deploys an optimized FF-YOLOv10 lightweight model on UAVs for rapid target localization, followed by an enhanced PP-YOLOv7 model on ground stations for precise detection. The FF-YOLOv10 model reduces computational complexity by 24.2% and increases inference speed by 33.3%, while the PP-YOLOv7 model achieves 94.2% precision with a 1.9% increase in recall rate. This framework provides an efficient and precise technical solution for the long-term ecological monitoring of marine endangered species, supporting habitat conservation policy formulation and ecosystem health assessments.