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

arXiv: https://arxiv.org/abs/2509.09297 · HTML: https://mlsanity.com/paper/2509.09297/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 32 references that are in the map.
- First posted on arXiv 11 Sep 2025.

## Builds on (most cited first, 19 of 19)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [DiffusionDet: Diffusion Model for Object Detection](https://mlsanity.com/paper/2211.09788/) (2022)
- [Deep Deterministic Uncertainty: A Simple Baseline](https://mlsanity.com/paper/2102.11582/) (2021)
- [Probabilistic two-stage detection](https://mlsanity.com/paper/2103.07461/) (2021)
- [Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection](https://mlsanity.com/paper/2405.10300/) (2024)
- [Uncertainty for Identifying Open-Set Errors in Visual Object Detection](https://mlsanity.com/paper/2104.01328/) (2021)
- [CertainNet: Sampling-free Uncertainty Estimation for Object Detection](https://mlsanity.com/paper/2110.01604/) (2021)
- [Hyperdimensional Feature Fusion for Out-Of-Distribution Detection](https://mlsanity.com/paper/2112.05341/) (2021)
- [AirTrack: Onboard Deep Learning Framework for Long-Range Aircraft Detection and Tracking](https://mlsanity.com/paper/2209.12849/) (2022)
- [Open-Set Object Detection Using Classification-free Object Proposal and Instance-level Contrastive Learning](https://mlsanity.com/paper/2211.11530/) (2022)
- [Open-set object detection: towards unified problem formulation and benchmarking](https://mlsanity.com/paper/2411.05564/) (2024)

## Built on by (most cited first, 1 of 1)

- [YOLOE-26: Integrating YOLO26 with YOLOE for Real-Time Open-Vocabulary Instance Segmentation](https://mlsanity.com/paper/2602.00168/) (2026)

## Abstract (arXiv)

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.