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arXiv Machine Learning By Spyridon Loukovitis, Anastasios Arsenos, Vasileios Karampinis, Athanasios Voulodimos

Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception

Read the original on arXiv Machine Learning →

The paper introduces a model‑agnostic open‑set detection framework for air‑to‑air visual object detection on UAVs, addressing the limitations of closed‑set detectors under domain shifts and flight data corruption. It estimates semantic uncertainty through entropy modeling in the embedding space and employs spectral normalization and temperature scaling to improve open‑set discrimination. Experiments on the AOT aerial benchmark and real‑world flight tests show up to a 10% relative AUROC improvement over standard YOLO detectors, with background rejection further enhancing robustness without sacrificing accuracy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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