The paper explores using generative models to translate RGB UAV images into synthetic infrared (IR) images for training vehicle detectors in domains where real IR data is scarce. Various translators—supervised GANs, ControlNet-based diffusion models, and LoRA-ed foundation models—were trained on paired RGB-IR datasets and applied to unseen target datasets to generate synthetic IR data. The synthetic IR images, especially those produced by Stable Diffusion 3.5 with ControlNet, significantly improved detection performance on unseen IR test sets, outperforming RGB and grayscale baselines and narrowing the gap to real IR data.
By Thijs A. Eker, Ella P. Fokkinga, Jan Erik van Woerden, Elfi I. S. Hofmeijer, Sebastiaan P. Snel, Klamer Schutte, Friso G. Heslinga
arXiv:2609.07780v2 Announce Type: replace
Abstract: Automated drone surveillance has become increasingly important for public safety, critical infrastructure protection,and restricted airspace monito...
By Ami Pandat, Rajasekhar Punna, Gopika Vinod, Rohit Shukla
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.
By Tanel Liiv, Sander Soodla, Nzamba Bignoumba, Alma M. Liezenga, Toomas Pruuden
The paper introduces the Wide-area Spatio-temporal Scene Understanding (WSTU) problem, which demands simultaneous wide-area coverage, per-target resolution, and temporal continuity—capabilities lacking in existing datasets. To address this, the authors present HARD, an ultra‑high‑resolution (12768×9564) UAV dataset annotated for object detection, multi‑object tracking, and scene‑level visual question answering. They also propose a latency‑aware metric, streaming‑HOTA (s‑HOTA), and show through baseline experiments that high resolution and processing latency significantly impact detection, tracking, and VQA performance, revealing gaps in current methods for WSTU.
By Yuhang Zhu, Meiyi Zhu, Yunkai Dang, Zhangnan Li, Yuxuan Wang, Wenbin Li, Hongbing Pan
arXiv:2608. 19866v1 Announce Type: new Abstract: This paper presents a novel data-driven approach to camera-based autonomy for micro-drones in GPS-denied, radio-challenging indoor environments.
By Niklas Voigt, Hartmut Surmann
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.
By Spyridon Loukovitis, Anastasios Arsenos, Vasileios Karampinis, Athanasios Voulodimos
The paper presents a multi‑campaign UAV thermal image dataset for inert ordnance screening, comprising 5,855 labeled image pairs collected in Tennessee across diverse terrains and seasons. The authors trained YOLOV11l and RT‑DETR‑R50 models on 33 m and 15 m altitude data, achieving automated candidate detection, and provided practical guidelines for future humanitarian mine action surveys. The dataset and models aim to aid screening and prioritization for follow‑up technical surveys or EOD assessment, not to replace clearance operations.
By Chad Melton, PhD., Annabelle Kelton
arXiv:2606. 26151v1 Announce Type: cross Abstract: While autonomous rovers have become indispensable to precision farming, achieving consistent operational safety remains a critical challenge.
By Th\'eo Biardeau (XLIM-ASALI, UFR SFA), Anne-Sophie Capelle-Laiz\'e (UP, XLIM-ASALI, XLIM-ASALI), Salwan Alwan (UFR SFA), David Helbert (UFR SFA)
arXiv:2607.17669v2 Announce Type: replace
Abstract: Drone-based object detection technology has advanced rapidly, becoming increasingly sophisticated and efficient. Recently, research trends have exp...
By Hyun-Ki Jung
arXiv:2607. 02718v1 Announce Type: cross Abstract: Recent advances in large-scale image generative models enable photorealistic scene synthesis with controllable attributes.
By Stanislav Panev, Minhyek Jeon, Vaishnavi Khindkar, Ahish Deshpande, Celso M de Melo, Shuowen Hu, Shayok Chakraborty, Fernando De la Torre
The paper investigates the use of evidential deep learning (EDL) for multi‑modal anti‑UAV detection, comparing it with sigmoid baselines, Dempster‑Shafer evidence fusion, and uncertainty‑driven temporal sensor gating across three benchmarks (thermal tracking, RGB‑audio‑RF classification, and RGB‑IR tracking). EDL improves accuracy by up to 5.9 percentage points and better ranks classification errors, while the other components (DS fusion, Dirichlet vacuity, temporal gating) do not provide the expected benefits. The study concludes that the primary advantage of EDL stems from its training objective rather than its uncertainty estimates.
By Dmitry Golovchits, Seyed Sahand Mohammadi Ziabari, Ali Mohammed Mansoor Alsahag
arXiv:2609.05523v1 Announce Type: cross
Abstract: This paper presents a one-stage learning framework that maps monocular roadside-camera images directly to vehicle states in a ground-fixed coordinate...
By Akos T. Kopeczi-Bocz, Tian Mi, Gabor Orosz, Denes Takacs