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InfoResearchPeer-reviewed
Visual Safety Mapping for UAV Landings Using Ordinal Regression Networks
- Published
- Record updated
Summary
Researchers introduce OR-SLZNet, a deep learning model based on ordinal regression that predicts safe landing zones from UAV imagery. It combines photometric cues such as color and texture with geometric cues such as flatness, slope, and depth to assign each pixel an ordinal safety score. The authors report real-time inference of about 0.02 s per frame and strong generalization across five diverse datasets.