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.
arXiv:2609.07780v2 Announce Type: replace Abstract: Automated drone surveillance has become increasingly important for public safety, critical infrastructure protection,and restricted airspace monito...
arXiv:2607. 02718v1 Announce Type: cross Abstract: Recent advances in large-scale image generative models enable photorealistic scene synthesis with controllable attributes.
arXiv:2610.07705v1 Announce Type: cross Abstract: The growing use of unmanned aerial vehicles (UAVs) has increased the importance of image-based UAV detection. Learning-based detectors are trained on...
The paper explores a synthetic-first training approach for detecting drones in medium- and long-wave infrared imagery, combining synthetic scene generation with fine-tuning on real data. It demonstrates that synthetic data can establish initial object representations, but real infrared data is crucial to close domain gaps and improve reliability. The study finds that aligning datasets has a greater impact on performance than increasing model size, and that semantic alignment in feature space is the strongest predictor of success, with radiometric factors like entropy and dynamic range also contributing.
arXiv:2608.21926v1 Announce Type: new Abstract: Unmanned aerial vehicle (UAV) navigation in modern low-altitude environments requires more accurate pose alignment in the final approach stage for targ...
The paper proposes a three-way open-set detection framework for autonomous navigation, classifying each detection as a known object, unknown object, or background based on a pretrained detector’s outputs. It introduces domain generalization and adaptation methods, evaluates them across various detector families and benchmarks, and demonstrates that this approach improves safety and efficiency in simulated navigation missions compared to binary detection methods.
arXiv:2606. 00747v1 Announce Type: cross Abstract: For low-altitude Unmanned Aerial Vehicle (UAV) autonomy, 3D spatial understanding is not merely a perception objective, but the safety interface between human instructions and physical flight.
CODE: Cross-Modal Calibration and Dynamic Suppression for Open World Object Detection introduces a unified inference-time framework that addresses semantic ambiguity and over-suppression in multimodal OWOD systems. It comprises Cross-Modal Joint Confidence Calibration, Uncertainty-Guided Universal Objectness Enhancement, and Dynamic Outlier Suppression via Confidence Margin. Experiments on the Real-World Detection benchmark with the OWL‑ViT L/14 backbone show CODE achieving 21.7 U‑mAP and 40.8 K‑mAP, surpassing prior state‑of‑the‑art results by 2.6 and 2.3 points respectively.
Aerial image-goal navigation requires an unmanned aerial vehicle (UAV) to reach a target location specified by a goal image. Existing world-model-based methods rank candidate trajectories using predicted futures, but typically rely on only one or a few point predictions, which is inadequate for large-scale outdoor environments with substantial future-state uncertainty.
arXiv:2609.22896v1 Announce Type: new Abstract: Autonomous vehicles operating in open-world scenarios are inevitably confronted with previously unknown objects, such as exotic animals or loose cargo....
arXiv:2609.13647v1 Announce Type: new Abstract: The rapid development of unmanned aerial vehicle (UAV) technology has made aerial-image object detection increasingly important for natural-resource mo...
arXiv:2508.13977v4 Announce Type: replace Abstract: Depth estimation is a fundamental component of spatial perception for autonomous driving and other unmanned systems operating in open urban environ...