2 papers
cs.CV2026
A unified three-branch deep learning framework for automated detection and counting of wildlife in aerial imagery
Zeyu Xu, Tiejun Wang, Andrew Skidmore +5
Automating aerial wildlife surveys requires reliable animal detection, consistent delineation of observation strips, and correction for repeated coverage between successive images.…
cs.CV2025
Automated Detection of Salvin's Albatrosses: Improving Deep Learning Tools for Aerial Wildlife Surveys
Mitchell Rogers, Theo Thompson, Isla Duporge +5
Recent advancements in deep learning and aerial imaging have transformed wildlife monitoring, enabling researchers to survey wildlife populations at unprecedented scales. Unmanned…