A unified three-branch deep learning framework for automated detection and counting of wildlife in aerial imagery
Automating aerial wildlife surveys requires reliable animal detection, consistent delineation of observation strips, and correction for repeated coverage between successive images. We present a unified three-branch deep learning framework that integrates these tasks through WildSeg, WildMatch and WildDet. WildSeg combines rod segmentation with geometric refinement to delineate counting strips from aircraft-mounted markers. WildMatch uses teacher-student distillation to learn dense image correspondences and identify overlapping survey areas without relying on geolocation metadata. WildDet combines dual-scale detection with feature fusion, density supervision and empty-image gating to suppo...
| Authors | Zeyu Xu, Tiejun Wang, Andrew Skidmore et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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