arXiv:2511.15343v1 Announce Type: cross 
Abstract: Developing reliable UAV navigation systems requires robust air-to-air object detectors capable of distinguishing between objects seen during training and previously unseen objects. While many methods address closed-set detection and achieve high-confidence recognition of in-domain (ID) targets, they generally do not tackle open-set detection, which requires simultaneous handling of both ID and out-of-distribution (OOD) objects. Existing open-set approaches typically rely on a single uncertainty score with thresholding, limiting flexibility and often conflating OOD objects with background clutter. In contrast, we propose a lightweight, model-agnostic post-processing framework that explicitly separates background from unknown objects while preserving the base detector's performance. Our approach extends open-set detection beyond binary ID/OOD classification to real-time three-way classification among ID targets, OOD objects, and background. To this end, we employ a fusion scheme that aggregates multiple confidence estimates and per-detection features using a compact multilayer perceptron (MLP). Incorporating different logit variants into the MLP consistently enhances performance across both binary and three-class classification without compromising throughput. Extensive ablation and comparative experiments confirm that our method surpasses threshold-based baselines in two-class classification by an average of 2.7% AUROC, while retaining or improving open-set mAP. Furthermore, our study uniquely enables robust three-class classification, a critical capability for safe UAV navigation, where OOD objects must be actively avoided and background regions safely ignored. Comparative analysis highlights that our method surpasses competitive techniques in AUROC across datasets, while improving closed-set mAP by up to 9 points, an 18% relative gain.

يتناول المقال إطارًا جديدًا للكشف عن الأجسام الجوية في بيئة مفتوحة باستخدام الطائرات بدون طيار (UAV). يتناول القيود المفروضة على الأساليب الحالية التي تركز بشكل أساسي على الكشف في بيئة مغلقة، والتي لا تميز بشكل فعال بين الأجسام المعروفة وغير المعروفة. يعزز النهج المقترح، المستقل عن النموذج، قدرات الكشف من خلال تمكين التصنيف في الوقت الحقيقي للأهداف المعروفة، والأجسام خارج التوزيع، والخلفية، مما يحسن موثوقية أنظمة الملاحة للطائرات بدون طيار.

El artículo presenta un nuevo marco para la detección de objetos aéreos en un entorno abierto utilizando UAVs. Aborda las limitaciones de los métodos existentes que se centran principalmente en la detección en un entorno cerrado, que no distinguen eficazmente entre objetos conocidos y desconocidos. El enfoque propuesto, independiente del modelo, mejora las capacidades de detección al permitir la clasificación en tiempo real de objetivos en dominio, objetos fuera de distribución y fondo, mejorando así la fiabilidad de los sistemas de navegación de UAV.

L'article présente un nouveau cadre pour la détection d'objets aériens en open-set utilisant des UAV. Il aborde les limites des méthodes existantes qui se concentrent principalement sur la détection en closed-set, ne distinguant pas efficacement les objets connus des objets inconnus. L'approche proposée, agnostique au modèle, améliore les capacités de détection en permettant une classification en temps réel des cibles in-domain, des objets hors distribution et de l'arrière-plan, améliorant ainsi la fiabilité des systèmes de navigation UAV.

The article discusses a new framework for open-set aerial object detection using UAVs. It addresses the limitations of existing methods that primarily focus on closed-set detection, which do not effectively distinguish between known and unknown objects. The proposed model-agnostic approach enhances detection capabilities by enabling real-time classification of in-domain targets, out-of-distribution objects, and background, thereby improving the reliability of UAV navigation systems.

Fast Post-Hoc Confidence Fusion for 3-Class Open-Set Aerial Object Detection

arXiv:2601.07975v1 Announce Type: new 
Abstract: High-resolution UAV photogrammetry has become a key technology for precision agriculture, enabling centimeter-level crop monitoring and point-level plant localization. However, point-level maize localization in UAV imagery remains challenging due to (1) extremely small object-to-pixel ratios, typically less than 0.1%, (2) prohibitive computational costs of quadratic attention on ultra-high-resolution images larger than 3000 x 4000 pixels, and (3) agricultural scene-specific complexities such as sparse object distribution and environmental variability that are poorly handled by general-purpose vision models.
  To address these challenges, we propose the Additive Kolmogorov-Arnold Transformer (AKT), which replaces conventional multilayer perceptrons with Pade Kolmogorov-Arnold Network (PKAN) modules to enhance functional expressivity for small-object feature extraction, and introduces PKAN Additive Attention (PAA) to model multiscale spatial dependencies with reduced computational complexity. In addition, we present the Point-based Maize Localization (PML) dataset, consisting of 1,928 high-resolution UAV images with approximately 501,000 point annotations collected under real field conditions.
  Extensive experiments show that AKT achieves an average F1-score of 62.8%, outperforming state-of-the-art methods by 4.2%, while reducing FLOPs by 12.6% and improving inference throughput by 20.7%. For downstream tasks, AKT attains a mean absolute error of 7.1 in stand counting and a root mean square error of 1.95-1.97 cm in interplant spacing estimation. These results demonstrate that integrating Kolmogorov-Arnold representation theory with efficient attention mechanisms offers an effective framework for high-resolution agricultural remote sensing.

يمثل تقديم المحول الإضافي كولموغوروف-أرنولد (AKT) تقدمًا كبيرًا في تحديد موقع الذرة على مستوى النقاط باستخدام صور الطائرات بدون طيار، حيث يتناول التحديات مثل النسب المنخفضة بين الكائنات والبكسل وتكاليف الحوسبة العالية المرتبطة بالنماذج التقليدية. تعزز هذه الطريقة المبتكرة استخراج الميزات للكائنات الصغيرة وتقدم نمذجة مكانية متعددة المقاييس من خلال الانتباه الإضافي PKAN (PAA).

La introducción del Transformador Aditivo Kolmogorov-Arnold (AKT) representa un avance significativo en la localización de maíz a nivel de puntos utilizando imágenes de UAV, abordando desafíos como las bajas proporciones objeto-píxel y los altos costos computacionales asociados con modelos tradicionales. Este enfoque innovador mejora la extracción de características para objetos pequeños e introduce un modelado espacial multiescalar a través de la Atención Aditiva PKAN (PAA).

L'introduction du Transformateur Additif Kolmogorov-Arnold (AKT) représente une avancée significative dans la localisation de maïs au niveau des points à l'aide d'images UAV, en répondant à des défis tels que les faibles rapports objet-pixel et les coûts computationnels élevés associés aux modèles traditionnels. Cette approche innovante améliore l'extraction des caractéristiques pour les petits objets et introduit une modélisation spatiale multiscale grâce à l'Attention Additive PKAN (PAA).

The introduction of the Additive Kolmogorov-Arnold Transformer (AKT) marks a significant advancement in point-level maize localization using UAV imagery, addressing challenges such as low object-to-pixel ratios and high computational costs associated with traditional models. This innovative approach enhances feature extraction for small objects and introduces multiscale spatial modeling through PKAN Additive Attention (PAA).

An Efficient Additive Kolmogorov-Arnold Transformer for Point-Level Maize Localization in Unmanned Aerial Vehicle Imagery

One More Thing in AI – Your Shortcut to AI Mastery

Fast Post-Hoc Confidence Fusion for 3-Class Open-Set Aerial Object Detection

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