Work

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

Year: 2025

Type: article

Abstract: ) 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.

Source: Animals

DOI: 10.3390/ani15213100

Authors: Jun Liu, Fengxiang Jin, Min Ji, Liang Qu, Juan Wang, Chen Wang

Institutions: Shandong University of Science and Technology, Ministry of Natural Resources

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Life below water

Open Access status: gold

Estimated APC paid: $2,896