Drone
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FSDETR: Frequency-Spatial Feature Enhancement for Small Object Detection
2026-04-16 11:22:13Jianchao Huang, Fengming Zhang, Haibo Zhu, Tao YanAbstract
Small object detection remains a significant challenge due to feature degradation from downsampling, mutual occlusion in dense clusters, and complex background interference. To address these issues, this paper proposes FSDETR, a frequency-spatial feature enhancement framework built upon the RT-DETR baseline. By establishing a collaborative modeling mechanism, the method effectively leverages complementary structural information. Specifically, a Spatial Hierarchical Attention Block (SHAB) captures both local details and global dependencies to strengthen semantic representation. Furthermore, to mitigate occlusion in dense scenes, the Deformable Attention-based Intra-scale Feature Interaction (DA-AIFI) focuses on informative regions via dynamic sampling. Finally, the Frequency-Spatial Feature Pyramid Network (FSFPN) integrates frequency filtering with spatial edge extraction via the Cross-domain Frequency-Spatial Block (CFSB) to preserve fine-grained details. Experimental results show that with only 14.7M parameters, FSDETR achieves 13.9% APS on VisDrone 2019 and 48.95% AP50 tiny on TinyPerson, showing strong performance on small-object benchmarks. The code and models are available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2604.14884
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Robust Energy-Aware Routing for Air-Ground Cooperative Multi-UAV Delivery in Wind-Uncertain Environments
2026-04-15 03:43:18Tianshun Li, Hongliang Lu, Yanggang Sheng, Zhongzhen Wang, Haoang Li, Xinhu ZhengAbstract
Ensuring energy feasibility under wind uncertainty is critical for the safety and reliability of UAV delivery missions. In realistic truck-drone logistics systems, UAVs must deliver parcels and safely return under time-varying wind conditions that are only partially observable during flight. However, most existing routing approaches assume static or deterministic energy models, making them unreliable in dynamic wind environments. We propose Battery-Efficient Routing (BER), an online risk-sensitive planning framework for wind-sensitive truck-assisted UAV delivery. The problem is formulated as routing on a time dependent energy graph whose edge costs evolve according to wind-induced aerodynamic effects. BER continuously evaluates return feasibility while balancing instantaneous energy expenditure and uncertainty-aware risk. The approach is embedded in a hierarchical aerial-ground delivery architecture that combines task allocation, routing, and decentralized trajectory execution. Extensive simulations on synthetic ER graphs generated in Unreal Engine environments and quasi-real wind logs demonstrate that BER significantly improves mission success rates and reduces wind-induced failures compared with static and greedy baselines. These results highlight the importance of integrating real-time energy budgeting and environmental awareness for UAV delivery planning under dynamic wind conditions.
Abstract (translated)
URL
https://arxiv.org/abs/2604.13441
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See&Say: Vision Language Guided Safe Zone Detection for Autonomous Package Delivery Drones
2026-04-14 20:54:31Mahyar Ghazanfari, Peng WeiAbstract
Autonomous drone delivery systems are rapidly advancing, but ensuring safe and reliable package drop-offs remains highly challenging in cluttered urban and suburban environments where accurately identifying suitable package drop zones is critical. Existing approaches typically rely on either geometry-based analysis or semantic segmentation alone, but these methods lack the integrated semantic reasoning required for robust decision-making. To address this gap, we propose See&Say, a novel framework that combines geometric safety cues with semantic perception, guided by a Vision-Language Model (VLM) for iterative refinement. The system fuses monocular depth gradients with open-vocabulary detection masks to produce safety maps, while the VLM dynamically adjusts object category prompts and refines hazard detection across time, enabling reliable reasoning under dynamic conditions during the final delivery phase. When the primary drop-pad is occupied or unsafe, the proposed See&Say also identifies alternative candidate zones for package delivery. We curated a dataset of urban delivery scenarios with moving objects and human activities to evaluate the approach. Experimental results show that See&Say outperforms all baselines, achieving the highest accuracy and IoU for safety map prediction as well as superior performance in alternative drop zone evaluation across multiple thresholds. These findings highlight the promise of VLM-guided segmentation-depth fusion for advancing safe and practical drone-based package delivery.
Abstract (translated)
URL
https://arxiv.org/abs/2604.13292
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DroneScan-YOLO: Redundancy-Aware Lightweight Detection for Tiny Objects in UAV Imagery
2026-04-14 20:14:57Yann V. BellecAbstract
Aerial object detection in UAV imagery presents unique challenges due to the high prevalence of tiny objects, adverse environmental conditions, and strict computational constraints. Standard YOLO-based detectors fail to address these jointly: their minimum detection stride of 8 pixels renders sub-32px objects nearly undetectable, their CIoU loss produces zero gradients for non-overlapping tiny boxes, and their architectures contain significant filter redundancy. We propose DroneScan-YOLO, a holistic system contribution that addresses these limitations through four coordinated design choices: (1) increased input resolution of 1280x1280 to maximize spatial detail for tiny objects, (2) RPA-Block, a dynamic filter pruning mechanism based on lazy cosine-similarity updates with a 10-epoch warm-up period, (3) MSFD, a lightweight P2 detection branch at stride 4 adding only 114,592 parameters (+1.1%), and (4) SAL-NWD, a hybrid loss combining Normalized Wasserstein Distance with size-adaptive CIoU weighting, integrated into YOLOv8's TaskAligned assignment pipeline. Evaluated on VisDrone2019-DET, DroneScan-YOLO achieves 55.3% mAP@50 and 35.6% mAP@50-95, outperforming the YOLOv8s baseline by +16.6 and +12.3 points respectively, improving recall from 0.374 to 0.518, and maintaining 96.7 FPS inference speed with only +4.1% parameters. Gains are most pronounced on tiny object classes: bicycle AP@50 improves from 0.114 to 0.328 (+187%), and awning-tricycle from 0.156 to 0.237 (+52%).
Abstract (translated)
URL
https://arxiv.org/abs/2604.13278
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A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models
2026-04-14 19:16:38Augustin de la Brosse, Damien Garreau, Thomas Houet, Thomas CorpettiAbstract
Mapping the spatial distribution of species is essential for conservation policy and invasive species management. Species distribution models (SDMs) are the primary tools for this task, serving two purposes: achieving robust predictive performance while providing ecological insights into the driving factors of distribution. However, the increasing complexity of deep learning SDMs has made extracting these insights more challenging. To reconcile these objectives, we propose the first implementation of concept-based Explainable AI (XAI) for SDMs. We leverage the Robust TCAV (Testing with Concept Activation Vectors) methodology to quantify the influence of landscape concepts on model predictions. To enable this, we provide a new open-access landscape concept dataset derived from high-resolution multispectral and LiDAR drone imagery. It includes 653 patches across 15 distinct landscape concepts and 1,450 random reference patches, designed to suit a wide range of species. We demonstrate this approach through a case study of two aquatic insects, Plecoptera and Trichoptera, using two Convolutional Neural Networks and one Vision Transformer. Results show that concept-based XAI helps validate SDMs against expert knowledge while uncovering novel associations that generate new ecological hypotheses. Robust TCAV also provides landscape-level information, useful for policy-making and land management. Code and datasets are publicly available.
Abstract (translated)
URL
https://arxiv.org/abs/2604.13240
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GeoLink: A 3D-Aware Framework Towards Better Generalization in Cross-View Geo-Localization
2026-04-14 18:06:41Hongyang Zhang, Yinhao Liu, Haitao Zhang, Zhongyi Wen, Shuxian Liang, Xiansheng HuaAbstract
Generalizable cross-view geo-localization aims to match the same location across views in unseen regions and conditions without GPS supervision. Its core difficulty lies in severe semantic inconsistency caused by viewpoint variation and poor generalization under domain shift. Existing methods mainly rely on 2D correspondence, but they are easily distracted by redundant shared information across views, leading to less transferable representations. To address this, we propose GeoLink, a 3D-aware semantic-consistent framework for Generalizable cross-view geo-localization. Specifically, we offline reconstruct scene point clouds from multi-view drone images using VGGT, providing stable structural priors. Based on these 3D anchors, we improve 2D representation learning in two complementary ways. A Geometric-aware Semantic Refinement module mitigates potentially redundant and view-biased dependencies in 2D features under 3D guidance. In addition, a Unified View Relation Distillation module transfers 3D structural relations to 2D features, improving cross-view alignment while preserving a 2D-only inference pipeline. Extensive experiments on multiple benchmarks show that GeoLink consistently outperforms state-of-the-art methods and achieves superior generalization across unseen domains and diverse weather environments.
Abstract (translated)
URL
https://arxiv.org/abs/2604.13183
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Dynamic Multi-Robot Task Allocation under Uncertainty and Communication Constraints: A Game-Theoretic Approach
2026-04-13 18:48:51Maria G. Mendoza, Pan-Yang Su, Bryce L. Ferguson, S. Shankar SastryAbstract
We study dynamic multi-robot task allocation under uncertain task completion, time-window constraints, and incomplete information. Tasks arrive online over a finite horizon and must be completed within specified deadlines, while agents operate from distributed hubs with limited sensing and communication. We model incomplete information through hub-based sensing regions that determine task visibility and a communication graph that governs inter-hub information exchange. Using this framework, we propose Iterative Best Response (IBR), a decentralized policy in which each agent selects the task that maximizes its marginal contribution to the locally observed welfare. We compare IBR against three baselines: Earliest Due Date first (EDD), Hungarian algorithm, and Stochastic Conflict-Based Allocation (SCoBA), on a city-scale package-delivery domain with up to 100 drones and varying task arrival scenarios. Under full and sparse communication, IBR achieves competitive task-completion performance with lower computation time.
Abstract (translated)
URL
https://arxiv.org/abs/2604.11954
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A3-FPN: Asymptotic Content-Aware Pyramid Attention Network for Dense Visual Prediction
2026-04-11 13:38:40Meng'en Qin, Yu Song, Quanling Zhao, Xiaodong Yang, Yingtao Che, Xiaohui YangAbstract
Learning multi-scale representations is the common strategy to tackle object scale variation in dense prediction tasks. Although existing feature pyramid networks have greatly advanced visual recognition, inherent design defects inhibit them from capturing discriminative features and recognizing small objects. In this work, we propose Asymptotic Content-Aware Pyramid Attention Network (A3-FPN), to augment multi-scale feature representation via the asymptotically disentangled framework and content-aware attention modules. Specifically, A3-FPN employs a horizontally-spread column network that enables asymptotically global feature interaction and disentangles each level from all hierarchical representations. In feature fusion, it collects supplementary content from the adjacent level to generate position-wise offsets and weights for context-aware resampling, and learns deep context reweights to improve intra-category similarity. In feature reassembly, it further strengthens intra-scale discriminative feature learning and reassembles redundant features based on information content and spatial variation of feature maps. Extensive experiments on MS COCO, VisDrone2019-DET and Cityscapes demonstrate that A3-FPN can be easily integrated into state-of-the-art CNN and Transformer-based architectures, yielding remarkable performance gains. Notably, when paired with OneFormer and Swin-L backbone, A3-FPN achieves 49.6 mask AP on MS COCO and 85.6 mIoU on Cityscapes. Codes are available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2604.10210
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Online Intention Prediction via Control-Informed Learning
2026-04-10 13:11:32Tianyu Zhou, Zihao Liang, Zehui Lu, Shaoshuai MouAbstract
This paper presents an online intention prediction framework for estimating the goal state of autonomous systems in real time, even when intention is time-varying, and system dynamics or objectives include unknown parameters. The problem is formulated as an inverse optimal control / inverse reinforcement learning task, with the intention treated as a parameter in the objective. A shifting horizon strategy discounts outdated information, while online control-informed learning enables efficient gradient computation and online parameter updates. Simulations under varying noise levels and hardware experiments on a quadrotor drone demonstrate that the proposed approach achieves accurate, adaptive intention prediction in complex environments.
Abstract (translated)
URL
https://arxiv.org/abs/2604.09303
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'Take Me Home, Wi-Fi Drone': A Drone-based Wireless System for Wilderness Search and Rescue
2026-04-10 08:52:16Weiying Hou, Luca Jiang-Tao Yu, Chenshu WuAbstract
Wilderness Search and Rescue (WiSAR) represents a longstanding and critical societal challenge, demanding innovative and automatic technological solutions. In this paper, we introduce Wi2SAR, a novel autonomous drone-based wireless system for long-range, through-occlusion WiSAR operations, without relying on existing infrastructure. Our basic insight is to leverage the automatic reconnection behavior of modern Wi-Fi devices to known networks. By mimicking these networks via on-drone Wi-Fi, Wi2SAR uniquely facilitates the discovery and localization of victims through their accompanying mobile devices. Translating this simple idea into a practical system poses substantial technical challenges. Wi2SAR overcomes these challenges via three distinct innovations: (1) a rapid and energy-efficient device discovery mechanism to discover and identify the target victim, (2) a novel RSS-only, long-range direction finding approach using a 3D-printed Luneburg Lens, amplifying the directional signal strength differences and significantly extending the operational range, and (3) an adaptive drone navigation scheme that guides the drone toward the target efficiently. We implement an end-to-end prototype and evaluate Wi2SAR across various mobile devices and real-world wilderness scenarios. Experimental results demonstrate Wi2SAR's high performance, efficiency, and practicality, highlighting its potential to advance autonomous WiSAR solutions. Wi2SAR is open-sourced at this https URL to facilitate further research and real-world deployment.
Abstract (translated)
URL
https://arxiv.org/abs/2604.09115
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Lightweight Multimodal Adaptation of Vision Language Models for Species Recognition and Habitat Context Interpretation in Drone Thermal Imagery
2026-04-07 17:36:01Hao Chen, Fang Qiu, Fangchao Dong, Defei Yang, Eve Bohnett, Li AnAbstract
This study proposes a lightweight multimodal adaptation framework to bridge the representation gap between RGB-pretrained VLMs and thermal infrared imagery, and demonstrates its practical utility using a real drone-collected dataset. A thermal dataset was developed from drone-collected imagery and was used to fine-tune VLMs through multimodal projector alignment, enabling the transfer of information from RGB-based visual representations to thermal radiometric inputs. Three representative models, including InternVL3-8B-Instruct, Qwen2.5-VL-7B-Instruct, and Qwen3-VL-8B-Instruct, were benchmarked under both closed-set and open-set prompting conditions for species recognition and instance enumeration. Among the tested models, Qwen3-VL-8B-Instruct with open-set prompting achieved the best overall performance, with F1 scores of 0.935 for deer, 0.915 for rhino, and 0.968 for elephant, and within-1 enumeration accuracies of 0.779, 0.982, and 1.000, respectively. In addition, combining thermal imagery with simultaneously collected RGB imagery enabled the model to generate habitat-context information, including land-cover characteristics, key landscape features, and visible human disturbance. Overall, the findings demonstrate that lightweight projector-based adaptation provides an effective and practical route for transferring RGB-pretrained VLMs to thermal drone imagery, expanding their utility from object-level recognition to habitat-context interpretation in ecological monitoring.
Abstract (translated)
本研究提出了一种轻量级多模态适应框架，用于弥合基于RGB预训练视觉语言模型与热红外影像之间的表征差距，并利用真实无人机采集数据集验证了其实用价值。研究从无人机影像中构建了热红外数据集，并通过多模态投影器对齐对视觉语言模型进行微调，实现了从基于RGB的视觉表征到热辐射测量输入的迁移。在封闭集与开放集提示条件下，对InternVL3-8B-Instruct、Qwen2.5-VL-7B-Instruct和Qwen3-VL-8B-Instruct三个代表性模型进行了物种识别与个体计数的基准测试。其中，采用开放集提示的Qwen3-VL-8B-Instruct模型综合表现最佳，在鹿、犀牛和大象的识别中F1分数分别达到0.935、0.915和0.968，误差在1以内的计数准确率分别为0.779、0.982和1.000。此外，结合同步采集的热红外与RGB影像，模型能够生成包含土地覆盖特征、关键景观要素及可见人为干扰的生境背景信息。总体而言，研究结果表明，基于投影器的轻量级适应方法为将RGB预训练视觉语言模型迁移至热红外无人机影像提供了有效且实用的路径，使其应用范围从目标级识别拓展至生态监测中的生境背景解读。
URL
https://arxiv.org/abs/2604.06124
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Precise Aggressive Aerial Maneuvers with Sensorimotor Policies
2026-04-07 13:00:59Tianyue Wu, Guangtong Xu, Zihan Wang, Junxiao Lin, Tianyang Chen, Yuze Wu, Zhichao Han, Zhiyang Liu, Fei GaoAbstract
Precise aggressive maneuvers with lightweight onboard sensors remains a key bottleneck in fully exploiting the maneuverability of drones. Such maneuvers are critical for expanding the systems' accessible area by navigating through narrow openings in the environment. Among the most relevant problems, a representative one is aggressive traversal through narrow gaps with quadrotors under SE(3) constraints, which require the quadrotors to leverage a momentary tilted attitude and the asymmetry of the airframe to navigate through gaps. In this paper, we achieve such maneuvers by developing sensorimotor policies directly mapping onboard vision and proprioception into low-level control commands. The policies are trained using reinforcement learning (RL) with end-to-end policy distillation in simulation. We mitigate the fundamental hardness of model-free RL's exploration on the restricted solution space with an initialization strategy leveraging trajectories generated by a model-based planner. Careful sim-to-real design allows the policy to control a quadrotor through narrow gaps with low clearances and high repeatability. For instance, the proposed method enables a quadrotor to navigate a rectangular gap at a 5 cm clearance, tilted at up to 90-degree orientation, without knowledge of the gap's position or orientation. Without training on dynamic gaps, the policy can reactively servo the quadrotor to traverse through a moving gap. The proposed method is also validated by training and deploying policies on challenging tracks of narrow gaps placed closely. The flexibility of the policy learning method is demonstrated by developing policies for geometrically diverse gaps, without relying on manually defined traversal poses and visual features.
Abstract (translated)
URL
https://arxiv.org/abs/2604.05828
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MARS-Dragonfly: Agile and Robust Flight Control of Modular Aerial Robot Systems
2026-04-07 06:43:08Rui Huang, Zhiqian Cai, Siyu Tang, Pengxuan Wei, Lidong Li, Xin Chen, Wenhan Cao, Zhenyu Zhang, Lin ZhaoAbstract
Modular Aerial Robot Systems (MARS) comprise multiple drone units with reconfigurable connected formations, providing high adaptability to diverse mission scenarios, fault conditions, and payload capacities. However, existing control algorithms for MARS rely on simplified quasi-static models and rule-based allocation, which generate discontinuous and unbounded motor commands. This leads to attitude error accumulation as the number of drone units scales, ultimately causing severe oscillations during docking, separation, and waypoint tracking. To address these limitations, we first design a compact mechanical system that enables passive docking, detection-free passive locking, and magnetic-assisted separation using a single micro servo. Second, we introduce a force-torque-equivalent and polytope-constraint virtual quadrotor that explicitly models feasible wrench sets. Together, these abstractions capture the full MARS dynamics and enable existing quadrotor controllers to be applied across different configurations. We further optimize the yaw angle that maximizes control authority to enhance agility. Third, building on this abstraction, we design a two-stage predictive-allocation pipeline: a constrained predictive tracker computes virtual inputs while respecting force/torque bounds, and a dynamic allocator maps these inputs to individual modules with balanced objectives to produce smooth, trackable motor commands. Simulations across over 10 configurations and real-world experiments demonstrate stable docking, locking, and separation, as well as effective control performance. To our knowledge, this is the first real-world demonstration of MARS achieving agile flight and transport with 40 deg peak pitch while maintaining an average position error of 0.0896 m. The video is available at: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2604.05499
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LLM-as-Judge for Semantic Judging of Powerline Segmentation in UAV Inspection
2026-04-07 03:16:44Akram Hossain, Rabab Abdelfattah, Xiaofeng Wang, Kareem AbdelfatahAbstract
The deployment of lightweight segmentation models on drones for autonomous power line inspection presents a critical challenge: maintaining reliable performance under real-world conditions that differ from training data. Although compact architectures such as U-Net enable real-time onboard inference, their segmentation outputs can degrade unpredictably in adverse environments, raising safety concerns. In this work, we study the feasibility of using a large language model (LLM) as a semantic judge to assess the reliability of power line segmentation results produced by drone-mounted models. Rather than introducing a new inspection system, we formalize a watchdog scenario in which an offboard LLM evaluates segmentation overlays and examine whether such a judge can be trusted to behave consistently and perceptually coherently. To this end, we design two evaluation protocols that analyze the judge's repeatability and sensitivity. First, we assess repeatability by repeatedly querying the LLM with identical inputs and fixed prompts, measuring the stability of its quality scores and confidence estimates. Second, we evaluate perceptual sensitivity by introducing controlled visual corruptions (fog, rain, snow, shadow, and sunflare) and analyzing how the judge's outputs respond to progressive degradation in segmentation quality. Our results show that the LLM produces highly consistent categorical judgments under identical conditions while exhibiting appropriate declines in confidence as visual reliability deteriorates. Moreover, the judge remains responsive to perceptual cues such as missing or misidentified power lines, even under challenging conditions. These findings suggest that, when carefully constrained, an LLM can serve as a reliable semantic judge for monitoring segmentation quality in safety-critical aerial inspection tasks.
Abstract (translated)
URL
https://arxiv.org/abs/2604.05371
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Indoor Asset Detection in Large Scale 360{deg} Drone-Captured Imagery via 3D Gaussian Splatting
2026-04-07 01:36:50Monica Tang, Avideh ZakhorAbstract
We present an approach for object-level detection and segmentation of target indoor assets in 3D Gaussian Splatting (3DGS) scenes, reconstructed from 360° drone-captured imagery. We introduce a 3D object codebook that jointly leverages mask semantics and spatial information of their corresponding Gaussian primitives to guide multi-view mask association and indoor asset detection. By integrating 2D object detection and segmentation models with semantically and spatially constrained merging procedures, our method aggregates masks from multiple views into coherent 3D object instances. Experiments on two large indoor scenes demonstrate reliable multi-view mask consistency, improving F1 score by 65% over state-of-the-art baselines, and accurate object-level 3D indoor asset detection, achieving an 11% mAP gain over baseline methods.
Abstract (translated)
本文提出一种针对3D高斯泼溅（3DGS）场景中目标室内资产的目标级检测与分割方法，该场景由无人机360°航拍图像重建而成。我们引入了一个3D目标码本，联合利用高斯基元对应的掩码语义与空间信息，以引导多视角掩码关联与室内资产检测。通过整合2D目标检测与分割模型，并结合语义与空间约束的合并流程，我们的方法将多视角掩码聚合成连贯的3D目标实例。在两个大型室内场景上的实验表明，该方法实现了可靠的多视角掩码一致性，较当前最优基线方法F1分数提升65%，并实现了精确的目标级3D室内资产检测，较基线方法mAP提升11%。
URL
https://arxiv.org/abs/2604.05316
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EDFNet: Early Fusion of Edge and Depth for Thin-Obstacle Segmentation in UAV Navigation
Abstract
Autonomous Unmanned Aerial Vehicles (UAVs) must reliably detect thin obstacles such as wires, poles, and branches to navigate safely in real-world environments. These structures remain difficult to perceive because they occupy few pixels, often exhibit weak visual contrast, and are strongly affected by class imbalance. Existing segmentation methods primarily target coarser obstacles and do not fully exploit the complementary multimodal cues needed for thin-structure perception. We present EDFNet, a modular early-fusion segmentation framework that integrates RGB, depth, and edge information for thin-obstacle perception in cluttered aerial scenes. We evaluate EDFNet on the Drone Depth and Obstacle Segmentation (DDOS) dataset across sixteen modality-backbone configurations using U-Net and DeepLabV3 in pretrained and non-pretrained settings. The results show that early RGB-Depth-Edge fusion provides a competitive and well-balanced baseline, with the most consistent gains appearing in boundary-sensitive and recall-oriented metrics. The pretrained RGBDE U-Net achieves the best overall performance, with the highest Thin-Structure Evaluation Score (0.244), mean IoU (0.219), and boundary IoU (0.234), while maintaining competitive runtime performance (19.62 FPS) on our evaluation hardware. However, performance on the rarest ultra-thin categories remains low across all models, indicating that reliable ultra-thin segmentation is still an open challenge. Overall, these findings position early RGB-Depth-Edge fusion as a practical and modular baseline for thin-obstacle segmentation in UAV navigation.
Abstract (translated)
URL
https://arxiv.org/abs/2604.09694
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SFFNet: Synergistic Feature Fusion Network With Dual-Domain Edge Enhancement for UAV Image Object Detection
2026-04-03 16:52:49Wenfeng Zhang, Jun Ni, Yue Meng, Xiaodong Pei, Wei Hu, Qibing Qin, Lei HuangAbstract
Object detection in unmanned aerial vehicle (UAV) images remains a highly challenging task, primarily caused by the complexity of background noise and the imbalance of target scales. Traditional methods easily struggle to effectively separate objects from intricate backgrounds and fail to fully leverage the rich multi-scale information contained within images. To address these issues, we have developed a synergistic feature fusion network (SFFNet) with dual-domain edge enhancement specifically tailored for object detection in UAV images. Firstly, the multi-scale dynamic dual-domain coupling (MDDC) module is designed. This component introduces a dual-driven edge extraction architecture that operates in both the frequency and spatial domains, enabling effective decoupling of multi-scale object edges from background noise. Secondly, to further enhance the representation capability of the model's neck in terms of both geometric and semantic information, a synergistic feature pyramid network (SFPN) is proposed. SFPN leverages linear deformable convolutions to adaptively capture irregular object shapes and establishes long-range contextual associations around targets through the designed wide-area perception module (WPM). Moreover, to adapt to the various applications or resource-constrained scenarios, six detectors of different scales (N/S/M/B/L/X) are designed. Experiments on two challenging aerial datasets (VisDrone and UAVDT) demonstrate the outstanding performance of SFFNet-X, achieving 36.8 AP and 20.6 AP, respectively. The lightweight models (N/S) also maintain a balance between detection accuracy and parameter efficiency. The code will be available at this https URL.
Abstract (translated)
无人机图像中的目标检测仍是一项极具挑战性的任务，主要源于背景噪声的复杂性和目标尺度的不平衡性。传统方法往往难以有效将目标从复杂背景中分离，且未能充分利用图像中丰富的多尺度信息。针对这些问题，我们开发了一种专为无人机图像目标检测设计的双域边缘增强协同特征融合网络（SFFNet）。首先，设计了多尺度动态双域耦合（MDDC）模块，该组件引入了在频域和空间域同时运行的双驱动边缘提取架构，能够有效将多尺度目标边缘与背景噪声解耦。其次，为进一步增强模型颈部在几何与语义信息上的表征能力，提出了协同特征金字塔网络（SFPN）。SFPN利用线性可变形卷积自适应捕捉不规则目标形状，并通过设计的广域感知模块（WPM）建立目标周围的远程上下文关联。此外，为适应不同应用场景或资源受限环境，还设计了六个不同尺度的检测器（N/S/M/B/L/X）。在两个具有挑战性的航拍数据集（VisDrone和UAVDT）上的实验表明，SFFNet-X分别取得了36.8 AP和20.6 AP的卓越性能，而轻量化模型（N/S）也在检测精度与参数效率间保持了良好平衡。代码将在以下网址公开：https://URL
URL
https://arxiv.org/abs/2604.03176
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Enhancing Multi-Robot Exploration Using Probabilistic Frontier Prioritization with Dirichlet Process Gaussian Mixtures
2026-04-03 13:51:23John Lewis Devassy, Meysam Basiri, M\'ario A. T. Figueiredo, Pedro U. LimaAbstract
Multi-agent autonomous exploration is essential for applications such as environmental monitoring, search and rescue, and industrial-scale surveillance. However, effective coordination under communication constraints remains a significant challenge. Frontier exploration algorithms analyze the boundary between the known and unknown regions to determine the next-best view that maximizes exploratory gain. This article proposes an enhancement to existing frontier-based exploration algorithms by introducing a probabilistic approach to frontier prioritization. By leveraging Dirichlet process Gaussian mixture model (DP-GMM) and a probabilistic formulation of information gain, the method improves the quality of frontier prioritization. The proposed enhancement, integrated into two state-of-the-art multi-agent exploration algorithms, consistently improves performance across environments of varying clutter, communication constraints, and team sizes. Simulations showcase an average gain of $10\%$ and $14\%$ for the two algorithms across all combinations. Successful deployment in real-world experiments with a dual-drone system further corroborates these findings.
Abstract (translated)
多智能体自主探索对于环境监测、搜救和工业级监控等应用至关重要。然而，在通信约束条件下实现有效协调仍是一项重大挑战。前沿探索算法通过分析已知区域与未知区域之间的边界，来确定能最大化探索收益的下一最佳观测点。本文提出通过引入概率性方法对前沿进行优先级排序，以增强现有基于前沿的探索算法。该方法通过利用狄利克雷过程高斯混合模型（DP-GMM）和信息收益的概率性建模，提升了前沿优先级排序的质量。该改进被集成到两种最先进的多智能体探索算法中，在杂乱程度不同、通信约束各异以及团队规模变化的各类环境中均持续提升性能。仿真实验表明，两种算法在所有组合场景中平均分别提升10%和14%的性能。双无人机系统在真实世界实验中的成功部署进一步 corroborates 了这些发现。
URL
https://arxiv.org/abs/2604.03042
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Vision-Based End-to-End Learning for UAV Traversal of Irregular Gaps via Differentiable Simulation
2026-04-03 06:44:10Linzuo Zhang, Yu Hu, Feng Yu, Yang Deng, Wenxian Yu, Danping ZouAbstract
-Navigation through narrow and irregular gaps is an essential skill in autonomous drones for applications such as inspection, search-and-rescue, and disaster response. However, traditional planning and control methods rely on explicit gap extraction and measurement, while recent end-to-end approaches often assume regularly shaped gaps, leading to poor generalization and limited practicality. In this work, we present a fully vision-based, end-to-end framework that maps depth images directly to control commands, enabling drones to traverse complex gaps within unseen environments. Operating in the Special Euclidean group SE(3), where position and orientation are tightly coupled, the framework leverages differentiable simulation, a Stop-Gradient operator, and a Bimodal Initialization Distribution to achieve stable traversal through consecutive gaps. Two auxiliary prediction modules-a gap-crossing success classifier and a traversability predictor-further enhance continuous navigation and safety. Extensive simulation and real-world experiments demonstrate the approach's effectiveness, generalization capability, and practical robustness.
Abstract (translated)
在自主无人机中，穿越狭窄且不规则间隙是一项关键技能，常用于检测、搜救和灾害响应等任务。然而，传统规划与控制方法依赖显式的间隙提取与测量，而近年端到端方法常假设间隙形状规则，导致泛化能力差且实用性有限。本研究提出一个完全基于视觉的端到端框架，直接将深度图像映射为控制指令，使无人机能够穿越未知环境中的复杂间隙。该框架在位置与姿态紧密耦合的特殊欧几里得群SE(3)中运行，通过可微分仿真、停止梯度算子及双峰初始化分布，实现连续间隙间的稳定穿越。两个辅助预测模块——间隙穿越成功分类器与可穿越性预测器——进一步提升了连续导航与安全性。大量仿真与真实实验验证了该方法的高效性、泛化能力及实际鲁棒性。
URL
https://arxiv.org/abs/2604.02779
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Satellite-Free Training for Drone-View Geo-Localization
2026-04-02 03:48:53Tao Liu, Yingzhi Zhang, Kan Ren, Xiaoqi ZhaoAbstract
Drone-view geo-localization (DVGL) aims to determine the location of drones in GPS-denied environments by retrieving the corresponding geotagged satellite tile from a reference gallery given UAV observations of a location. In many existing formulations, these observations are represented by a single oblique UAV image. In contrast, our satellite-free setting is designed for multi-view UAV sequences, which are used to construct a geometry-normalized UAV-side location representation before cross-view retrieval. Existing approaches rely on satellite imagery during training, either through paired supervision or unsupervised alignment, which limits practical deployment when satellite data are unavailable or restricted. In this paper, we propose a satellite-free training (SFT) framework that converts drone imagery into cross-view compatible representations through three main stages: drone-side 3D scene reconstruction, geometry-based pseudo-orthophoto generation, and satellite-free feature aggregation for retrieval. Specifically, we first reconstruct dense 3D scenes from multi-view drone images using 3D Gaussian splatting and project the reconstructed geometry into pseudo-orthophotos via PCA-guided orthographic projection. This rendering stage operates directly on reconstructed scene geometry without requiring camera parameters at rendering time. Next, we refine these orthophotos with lightweight geometry-guided inpainting to obtain texture-complete drone-side views. Finally, we extract DINOv3 patch features from the generated orthophotos, learn a Fisher vector aggregation model solely from drone data, and reuse it at test time to encode satellite tiles for cross-view retrieval. Experimental results on University-1652 and SUES-200 show that our SFT framework substantially outperforms satellite-free generalization baselines and narrows the gap to methods trained with satellite imagery.
Abstract (translated)
无人机视角地理定位（DVGL）旨在通过从参考图库中检索对应的地理标记卫星瓦片，来确定无人机在GPS拒止环境中的位置，其输入是无人机对某地点的观测。在许多现有方案中，这些观测由单张倾斜无人机图像表示。相比之下，本文设计的无卫星场景适用于多视角无人机序列，用于在跨视角检索前构建几何归一化的无人机端位置表示。现有方法在训练阶段依赖卫星影像，无论是通过配对监督还是无监督对齐，这限制了卫星数据不可用或受限时的实际部署。本文提出一种无卫星训练（SFT）框架，通过三个主要阶段将无人机影像转换为跨视角兼容表示：无人机端三维场景重建、基于几何的伪正射影像生成，以及用于检索的无卫星特征聚合。具体而言，首先使用三维高斯泼溅（3D Gaussian splatting）从多视角无人机图像重建稠密三维场景，并通过PCA引导的正射投影将重建几何投影为伪正射影像。该渲染阶段直接基于重建的场景几何运行，无需渲染时的相机参数。随后，使用轻量级几何引导修复技术细化这些正射影像，以获得纹理完整的无人机端视图。最后，从生成的正射影像中提取DINOv3块特征，仅从无人机数据学习Fisher向量聚合模型，并在测试时复用该模型对卫星瓦片进行编码以实现跨视角检索。在University-1652和SUES-200数据集上的实验结果表明，所提SFT框架显著优于无卫星泛化基线，并缩小了与使用卫星影像训练的方法之间的性能差距。
URL
https://arxiv.org/abs/2604.01581
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