提升无人机空中视觉检测在未知目标和数据损坏下的可靠性
Model-Agnostic Open-Set Air-to-Air Visual Object Detection for Reliable UAV Perception
- 基于嵌入空间熵建模，显式识别未知目标并抗飞行数据损坏
- 在AOT基准上实现比标准YOLO检测器高10%的AUROC提升
- 适合对安全性要求高的动态空对空无人机感知场景
开放集检测对真实环境下无人机空对空目标检测的鲁棒性至关重要。传统闭集检测器在域偏移和飞行数据损坏下性能显著下降，威胁安全关键应用。本文提出一种专为基于嵌入的检测器设计的模型无关开放集检测框架，显式处理未知目标拒绝问题，并增强对损坏飞行数据的鲁棒性。该方法通过嵌入空间中的熵建模估计语义不确定性，结合谱归一化与温度缩放提升开放集判别能力。我们在具有挑战性的AOT空中基准数据集上验证了该方法，并进行了广泛的实机飞行测试。全面的消融实验表明，相比基线方法有持续改进，在标准YOLO检测器基础上实现最高10%的相对AUROC提升。此外，背景排斥进一步增强了鲁棒性且不损害检测精度，使本方案特别适用于动态空对空环境中的可靠无人机感知。
原文摘要 · Abstract (English)
Open-set detection is crucial for robust UAV autonomy in air-to-air object detection under real-world conditions. Traditional closed-set detectors degrade significantly under domain shifts and flight data corruption, posing risks to safety-critical applications. We propose a novel, model-agnostic open-set detection framework designed specifically for embedding-based detectors. The method explicitly handles unknown object rejection while maintaining robustness against corrupted flight data. It estimates semantic uncertainty via entropy modeling in the embedding space and incorporates spectral normalization and temperature scaling to enhance open-set discrimination. We validate our approach on the challenging AOT aerial benchmark and through extensive real-world flight tests. Comprehensive ablation studies demonstrate consistent improvements over baseline methods, achieving up to a 10\% relative AUROC gain compared to standard YOLO-based detectors. Additionally, we show that background rejection further strengthens robustness without compromising detection accuracy, making our solution particularly well-suited for reliable UAV perception in dynamic air-to-air environments.
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