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:2607. 02718v1 Announce Type: cross Abstract: Recent advances in large-scale image generative models enable photorealistic scene synthesis with controllable attributes.
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...
arXiv:2511.15343v2 Announce Type: replace-cross Abstract: Autonomous navigation in complex scenes requires reliable perception across scenarios that the model did not encounter during its training. A...
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.
arXiv:2608.27214v1 Announce Type: new Abstract: Open World Object Detection (OWOD) built on multimodal foundation models often suffers from semantic ambiguity caused by unidirectional text-to-vision...
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:2608. 09564v1 Announce Type: cross Abstract: UAV vision-language navigation (UAV-VLN) focuses on enabling an aerial agent to follow natural-language instructions in open 3D environments from egocentric visual observations.
The paper introduces a saliency-depth conditioning approach for zero‑shot segmentation of communication‑tower components in cluttered UAV imagery. By combining appearance‑based saliency with monocular relative depth, the method creates a coarse tower prior that suppresses irrelevant background, and integrates this module with Grounded‑SAM and SAM 3 to produce SD‑Grounded‑SAM and SD‑SAM 3. Experiments on the TOW‑300 dataset show that SD‑SAM 3 achieves the best instance‑segmentation performance while SD‑Grounded‑SAM reduces false positives, with ablations confirming the complementary benefits of saliency, depth, and box refinement.
arXiv:2608. 08308v1 Announce Type: cross Abstract: Modern vision systems must operate in "open-world" settings, where models must recognize known categories and detect unseen or anomalous content.
arXiv:2606. 15468v1 Announce Type: cross Abstract: Vision models can achieve strong performance on classification tasks, but the internal representations supporting their predictions are often difficult to interpret.
arXiv:2606. 24353v1 Announce Type: cross Abstract: Bird's-eye view (BEV) perception fuses multi-camera images into a unified top-down representation for autonomous driving.