A unified three-branch deep learning framework for automated detection and counting of wildlife in aerial imagery
We present a unified three-branch deep learning framework that integrates these tasks through WildSeg, WildMatch and WildDet.
ProofPaper ↗
Key points
- Automating aerial wildlife surveys requires reliable animal detection, consistent delineation of observation strips, and correction for repeated coverage between successive images.
- WildSeg combines rod segmentation with geometric refinement to delineate counting strips from aircraft-mounted markers.
- WildDet combines dual-scale detection with feature fusion, density supervision and empty-image gating to support animal detection and counting.
- We evaluated the framework using oblique aerial imagery of four antelope species from the Great Nile Migration Landscape in South Sudan.
Sources (1)
- [1]A unified three-branch deep learning framework for automated detection and counting of wildlife in aerial imageryarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 04:30 PM
We present a unified three-branch deep learning framework that integrates these tasks through WildSeg, WildMatch and WildDet.
Automating aerial wildlife surveys requires reliable animal detection, consistent delineation of observation strips, and correction for repeated coverage between successive images.
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