Drone
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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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VRUD: A Drone Dataset for Complex Vehicle-VRU Interactions within Mixed Traffic
2026-04-01 16:53:44Ziyu Wang, Hongrui Kou, Cheng Wang, Ruochen Li, Hubert P. H. Shum, Amir Atapour-Abarghouei, Yuxin ZhangAbstract
The Operational Design Domain (ODD) of urbanoriented Level 4 (L4) autonomous driving, especially for autonomous robotaxis, confronts formidable challenges in complex urban mixed traffic environments. These challenges stem mainly from the high density of Vulnerable Road Users (VRUs) and their highly uncertain and unpredictable interaction behaviors. However, existing open-source datasets predominantly focus on structured scenarios such as highways or regulated intersections, leaving a critical gap in data representing chaotic, unstructured urban environments. To address this, this paper proposes an efficient, high-precision method for constructing drone-based datasets and establishes the Vehicle-Vulnerable Road User Interaction Dataset (VRUD), as illustrated in Figure 1. Distinct from prior works, VRUD is collected from typical "Urban Villages" in Shenzhen, characterized by loose traffic supervision and extreme occlusion. The dataset comprises 4 hours of 4K/30Hz recording, containing 11,479 VRU trajectories and 1,939 vehicle trajectories. A key characteristic of VRUD is its composition: VRUs account for about 87% of all traffic participants, significantly exceeding the proportions in existing benchmarks. Furthermore, unlike datasets that only provide raw trajectories, we extracted 4,002 multi-agent interaction scenarios based on a novel Vector Time to Collision (VTTC) threshold, supported by standard OpenDRIVE HD maps. This study provides valuable, rare edge-case resources for enhancing the safety performance of ADS in complex, unstructured urban environments. To facilitate further research, we have made the VRUD dataset open-source at: this https URL.
Abstract (translated)
面向城市环境的L4级自动驾驶运营设计域（ODD），尤其是自动驾驶出租车，在复杂的城市混合交通环境中面临严峻挑战。这些挑战主要源于弱势道路使用者（VRU）的高密度分布及其高度不确定、难以预测的交互行为。然而，现有开源数据集多聚焦于高速公路或规范路口等结构化场景，缺乏代表混乱、非结构化城市环境的关键数据。为此，本文提出一种高效高精度的无人机数据集构建方法，并建立了如图1所示的车辆-弱势道路使用者交互数据集（VRUD）。与既往研究不同，VRUD采集自深圳典型"城中村"区域，这些区域交通监管松散且遮挡现象极端。数据集包含4小时4K/30Hz录像，涵盖11,479条VRU轨迹与1,939条车辆轨迹。VRUD的核心特征在于其构成：VRU占所有交通参与者的约87%，显著高于现有基准数据集的比例。此外，不同于仅提供原始轨迹的数据集，我们基于新型矢量碰撞时间（VTTC）阈值，结合标准OpenDRIVE高清地图，提取了4,002个多智能体交互场景。本研究为提升自动驾驶系统在复杂非结构化城市环境中的安全性能提供了宝贵的边缘案例资源。为促进后续研究，VRUD数据集已开源公开：此https链接。
URL
https://arxiv.org/abs/2604.01134
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BlankSkip: Early-exit Object Detection onboard Nano-drones
2026-03-30 08:14:46Carlo Marra, Beatrice Alessandra Motetti, Alessio Burrello, Enrico Macii, Massimo Poncino, Daniele Jahier PagliariAbstract
Deploying tiny computer vision Deep Neural Networks (DNNs) on-board nano-sized drones is key for achieving autonomy, but is complicated by the extremely tight constraints of their computational platforms (approximately 10 MiB memory, 1 W power budget). Early-exit adaptive DNNs that dial down the computational effort for "easy-to-process" input frames represent a promising way to reduce the average inference latency. However, while this approach is extensively studied for classification, its application to dense tasks like object detection (OD) is not straightforward. In this paper, we propose BlankSkip, an adaptive network for on-device OD that leverages a simple auxiliary classification task for early exit, i.e., identifying frames with no objects of interest. With experiments using a real-world nano-drone platform, the Bitcraze Crazyflie 2.1, we achieve up to 24% average throughput improvement with a limited 0.015 mean Average Precision (mAP) drop compared to a static MobileNet-SSD detector, on a state-of-the-art nano-drones OD dataset.
Abstract (translated)
URL
https://arxiv.org/abs/2603.28149
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CARLA-Air: Fly Drones Inside a CARLA World -- A Unified Infrastructure for Air-Ground Embodied Intelligence
2026-03-30 04:49:29Tianle Zeng, Hanxuan Chen, Yanci Wen, Hong ZhangAbstract
The convergence of low-altitude economies, embodied intelligence, and air-ground cooperative systems creates growing demand for simulation infrastructure capable of jointly modeling aerial and ground agents within a single physically coherent environment. Existing open-source platforms remain domain-segregated: driving simulators lack aerial dynamics, while multirotor simulators lack realistic ground scenes. Bridge-based co-simulation introduces synchronization overhead and cannot guarantee strict spatial-temporal consistency. We present CARLA-Air, an open-source infrastructure that unifies high-fidelity urban driving and physics-accurate multirotor flight within a single Unreal Engine process. The platform preserves both CARLA and AirSim native Python APIs and ROS 2 interfaces, enabling zero-modification code reuse. Within a shared physics tick and rendering pipeline, CARLA-Air delivers photorealistic environments with rule-compliant traffic, socially-aware pedestrians, and aerodynamically consistent UAV dynamics, synchronously capturing up to 18 sensor modalities across all platforms at each tick. The platform supports representative air-ground embodied intelligence workloads spanning cooperation, embodied navigation and vision-language action, multi-modal perception and dataset construction, and reinforcement-learning-based policy training. An extensible asset pipeline allows integration of custom robot platforms into the shared world. By inheriting AirSim's aerial capabilities -- whose upstream development has been archived -- CARLA-Air ensures this widely adopted flight stack continues to evolve within a modern infrastructure. Released with prebuilt binaries and full source: this https URL
Abstract (translated)
低空经济、具身智能与空地协同系统的融合，催生了对能够在单一物理一致环境中联合建模空中与地面智能体的仿真基础设施的日益增长需求。现有开源平台仍存在领域割裂：驾驶模拟器缺乏空中动力学，而多旋翼模拟器则缺乏真实地面场景。基于桥接的联合仿真会引入同步开销，且无法保证严格时空一致性。本文提出CARLA-Air，这是一个开源基础设施，可在单一Unreal Engine进程中统一高保真度城市驾驶与物理精确的多旋翼飞行。该平台保留了CARLA与AirSim的原生Python API及ROS 2接口，实现零修改代码复用。在共享物理 ticking 与渲染管线中，CARLA-Air提供符合交通规则的真实感环境、具备社会意识的行人模拟以及空气动力学一致的无人机动力学，每帧同步捕捉所有平台最多18种传感器模态。平台支持代表性的空地具身智能工作负载，涵盖协同、具身导航与视觉-语言动作、多模态感知与数据集构建，以及基于强化学习的策略训练。可扩展的资源管线支持将自定义机器人平台集成至共享世界。通过继承AirSim的空中能力（其上游开发已归档），CARLA-Air确保这一广泛采用的飞行栈能在现代基础设施中持续演进。发布包含预编译二进制文件及完整源代码：此https链接
URL
https://arxiv.org/abs/2603.28032
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Meta-Adaptive Beam Search Planning for Transformer-Based Reinforcement Learning Control of UAVs with Overhead Manipulators under Flight Disturbances
2026-03-27 17:08:40Hazim Alzorgan, Sayed Pedram Haeri Boroujeni, Abolfazl RaziAbstract
Drones equipped with overhead manipulators offer unique capabilities for inspection, maintenance, and contact-based interaction. However, the motion of the drone and its manipulator is tightly linked, and even small attitude changes caused by wind or control imperfections shift the end-effector away from its intended path. This coupling makes reliable tracking difficult and also limits the direct use of learning-based arm controllers that were originally designed for fixed-base robots. These effects appear consistently in our tests whenever the UAV body experiences drift or rapid attitude corrections. To address this behavior, we develop a reinforcement-learning (RL) framework with a transformer-based double deep Q learning (DDQN), with the core idea of using an adaptive beam-search planner that applies a short-horizon beam search over candidate control sequences using the learned critic as the forward estimator. This allows the controller to anticipate the end-effector's motion through simulated rollouts rather than executing those actions directly on the actual model, realizing a software-in-the-loop (SITL) approach. The lookahead relies on value estimates from a Transformer critic that processes short sequences of states, while a DDQN backbone provides the one-step targets needed to keep the learning process stable. Evaluated on a 3-DoF aerial manipulator under identical training conditions, the proposed meta-adaptive planner shows the strongest overall performance with a 10.2% reward increase, a substantial reduction in mean tracking error (from about 6% to 3%), and a 29.6% improvement in the combined reward-error metric relative to the DDQN baseline. Our method exhibits elevated stability in tracking target tip trajectory (by maintaining 5 cm tracking error) when the drone base exhibits drifts due to external disturbances, as opposed to the fixed-beam and Transformer-only variants.
Abstract (translated)
配备顶部机械臂的无人机在检测、维护及接触式交互方面具有独特能力。然而，无人机本体与机械臂的运动紧密耦合，即便由风况或控制偏差引起的微小姿态变化，也会导致末端执行器偏离预定轨迹。这种耦合不仅使可靠跟踪变得困难，也限制了最初为固定基座机器人设计的学习型机械臂控制器的直接应用。在我们的测试中，每当无人机本体出现漂移或快速姿态修正时，上述效应便会持续显现。为应对此问题，我们开发了一种基于Transformer的双深度Q学习（DDQN）强化学习框架，其核心思想是采用自适应波束搜索规划器：利用已学习的评判网络作为前向估计器，对候选控制序列进行短时域波束搜索。这使控制器能够通过模拟推演而非在实际模型上直接执行来预判末端执行器运动，从而实现软件在环（SITL）方法。该前瞻机制依赖于处理状态短序列的Transformer评判网络提供的价值估计，而DDQN主干网络则提供维持学习过程稳定所需的一步目标。在相同训练条件下对三自由度空中机械臂进行评估，所提出的元自适应规划器展现出最优综合性能：奖励提升10.2%，平均跟踪误差显著降低（从约6%降至3%），且相较于DDQN基线，综合奖励-误差指标改善29.6%。当无人机基座因外部扰动产生漂移时，本方法在跟踪末端轨迹方面表现出更高稳定性（可将跟踪误差维持在5厘米内），优于固定波束及纯Transformer变体方案。
URL
https://arxiv.org/abs/2603.26612
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SDD-YOLO: A Small-Target Detection Framework for Ground-to-Air Anti-UAV Surveillance with Edge-Efficient Deployment
2026-03-26 09:17:39Pengyu Chen, Haotian Sa, Yiwei Hu, Yuhan Cheng, Junbo WangAbstract
Detecting small unmanned aerial vehicles (UAVs) from a ground-to-air (G2A) perspective presents significant challenges, including extremely low pixel occupancy, cluttered aerial backgrounds, and strict real-time constraints. Existing YOLO-based detectors are primarily optimized for general object detection and often lack adequate feature resolution for sub-pixel targets, while introducing complexities during deployment. In this paper, we propose SDD-YOLO, a small-target detection framework tailored for G2A anti-UAV surveillance. To capture fine-grained spatial details critical for micro-targets, SDD-YOLO introduces a P2 high-resolution detection head operating at 4 times downsampling. Furthermore, we integrate the recent architectural advancements from YOLO26, including a DFL-free, NMS-free architecture for streamlined inference, and the MuSGD hybrid training strategy with ProgLoss and STAL, which substantially mitigates gradient oscillation on sparse small-target signals. To support our evaluation, we construct DroneSOD-30K, a large-scale G2A dataset comprising approximately 30,000 annotated images covering diverse meteorological conditions. Experiments demonstrate that SDD-YOLO-n achieves a mAP@0.5 of 86.0% on DroneSOD-30K, surpassing the YOLOv5n baseline by 7.8 percentage points. Extensive inference analysis shows our model attains 226 FPS on an NVIDIA RTX 5090 and 35 FPS on an Intel Xeon CPU, demonstrating exceptional efficiency for future edge deployment.
Abstract (translated)
从地对空（G2A）视角检测小型无人机（UAV）面临显著挑战，包括极低的像素占比、杂乱的空中背景以及严格的实时性要求。现有基于YOLO的检测器主要针对通用目标检测进行优化，往往缺乏对亚像素目标的足够特征分辨率，同时在部署时引入复杂性。本文提出SDD-YOLO，一个专为G2A反无人机监控设计的小目标检测框架。为捕捉微目标所需的关键细粒度空间细节，SDD-YOLO引入了在4倍下采样率下运行的P2高分辨率检测头。此外，我们整合了YOLO26的最新架构进展，包括无DFL、无NMS的简化推理架构，以及结合渐进损失（ProgLoss）和空间时间自适应学习（STAL）的MuSGD混合训练策略，该策略显著缓解了稀疏小目标信号上的梯度振荡问题。为支持评估，我们构建了DroneSOD-30K数据集，这是一个包含约3万张标注图像的大规模G2A数据集，覆盖多样气象条件。实验表明，SDD-YOLO-n在DroneSOD-30K上达到86.0%的mAP@0.5，比YOLOv5n基线高出7.8个百分点。广泛的推理分析显示，我们的模型在NVIDIA RTX 5090上达到226 FPS，在Intel Xeon CPU上达到35 FPS，展现了面向未来边缘部署的卓越效率。
URL
https://arxiv.org/abs/2603.25218
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Integrated Multi-Drone Task Allocation, Sequencing, and Optimal Trajectory Generation in Obstacle-Rich 3D Environments
2026-03-26 00:46:26Yunes Alqudsi, Murat MakaraciAbstract
Coordinating teams of aerial robots in cluttered three-dimensional (3D) environments requires a principled integration of discrete mission planning-deciding which robot serves which goals and in what order -- with continuous-time trajectory synthesis that enforces collision avoidance and dynamic feasibility. This paper introduces IMD-TAPP (Integrated Multi-Drone Task Allocation and Path Planning), an end-to-end framework that jointly addresses multi-goal allocation, tour sequencing, and safe trajectory generation for quadrotor teams operating in obstacle-rich spaces. IMD--TAPP first discretizes the workspace into a 3D navigation graph and computes obstacle-aware robot-to-goal and goal-to-goal travel costs via graph-search-based pathfinding. These costs are then embedded within an Injected Particle Swarm Optimization (IPSO) scheme, guided by multiple linear assignment, to efficiently explore coupled assignment/ordering alternatives and to minimize mission makespan. Finally, the resulting waypoint tours are transformed into time-parameterized minimum-snap trajectories through a generation-and-optimization routine equipped with iterative validation of obstacle clearance and inter-robot separation, triggering re-planning when safety margins are violated. Extensive MATLAB simulations across cluttered 3D scenarios demonstrate that IMD--TAPP consistently produces dynamically feasible, collision-free trajectories while achieving competitive completion times. In a representative case study with two drones serving multiple goals, the proposed approach attains a minimum mission time of 136~s while maintaining the required safety constraints throughout execution.
Abstract (translated)
URL
https://arxiv.org/abs/2603.24908
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Towards automatic smoke detector inspection: Recognition of the smoke detectors in industrial facilities and preparation for future drone integration
2026-03-25 22:30:43Lukas Kratochvila, Jakub Stefansky, Simon Bilik, Robert Rous, Tomas Zemcik, Michal Wolny, Frantisek Rusnak, Ondrej Cech, Karel HorakAbstract
Fire safety consists of a complex pipeline, and it is a very important topic of concern. One of its frontal parts are the smoke detectors, which are supposed to provide an alarm prior to a massive fire appears. As they are often difficult to reach due to high ceilings or problematic locations, an automatic inspection system would be very beneficial as it could allow faster revisions, prevent workers from dangerous work in heights, and make the whole process cheaper. In this study, we present the smoke detector recognition part of the automatic inspection system, which could easily be integrated to the drone system. As part of our research, we compare two popular convolutional-based object detectors YOLOv11 and SSD widely used on embedded devices together with the state-of-the-art transformer-based RT-DETRv2 with the backbones of different sizes. Due to a complicated way of collecting a sufficient amount of data for training in the real-world environment, we also compare several training strategies using the real and semi-synthetic data together with various augmentation methods. To achieve a robust testing, all models were evaluated on two test datasets with an expected and difficult appearance of the smoke detectors including motion blur, small resolution, or not complete objects. The best performing detector is the YOLOv11n, which reaches the average mAP@0.5 score of 0.884. Our code, pretrained models and dataset are publicly available.
Abstract (translated)
消防安全涉及复杂的管道系统，是一个非常重要的关注课题。其前端部件之一是烟雾探测器，旨在火灾大规模发生前发出警报。由于高天花板或位置特殊导致探测器往往难以触及，自动检测系统将非常有益——它能实现更快速的检修、避免工人在高空危险作业，并降低整体成本。本研究提出了可轻松集成至无人机系统的自动检测方案中的烟雾探测器识别部分。作为研究的一部分，我们比较了两种广泛用于嵌入式设备的流行卷积目标检测器YOLOv11与SSD，以及采用不同尺寸主干网络的最先进Transformer架构检测器RT-DETRv2。由于在真实环境中采集足量训练数据过程复杂，我们还对比了使用真实数据与半合成数据的多种训练策略，并结合不同数据增强方法。为进行稳健测试，所有模型在两个测试数据集上进行了评估，这些数据集包含烟雾探测器在预期及困难场景下的出现情况，如运动模糊、低分辨率或不完整物体。表现最佳的检测器是YOLOv11n，其平均mAP@0.5得分达到0.884。本研究代码、预训练模型及数据集均已公开。
URL
https://arxiv.org/abs/2603.24850
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Enhancing Drone Light Shows Performances: Optimal Allocation and Trajectories for Swarm Drone Formations
Abstract
Drone light shows (DLShows) represent a rapidly growing application of swarm robotics, creating captivating aerial displays through the synchronized flight of hundreds or thousands of unmanned aerial vehicles (UAVs) as environmentally friendly and reusable alternatives to traditional pyrotechnics. This domain presents unique challenges in optimally assigning drones to visual waypoints and generating smooth, collision-free trajectories at a very large scale. This article introduces the Unified Assignment and Trajectory Generation (UATG) framework. The proposed approach concurrently solves two core problems: the optimal assignment of drones to designated goal locations and the generation of dynamically feasible, collision-free, time-parameterized trajectories. The UATG framework is specifically designed for DLShows, ensuring minimal transition times between formations and guaranteeing inter-drone collision avoidance. A key innovation is its exceptional computational efficiency, enabling the coordination of large-scale in real-time; for instance, it computes the optimal assignment and trajectories for 1008 drones in approximately one second on a standard laptop. Extensive simulations in realistic environments validate the framework's performance, demonstrating its capability to orchestrate complex formations, from alphanumeric characters to intricate 3D shapes, with precision and visual smoothness. This work provides a critical advancement for the DLShow industry, offering a practical and scalable solution for generating complex aerial choreography and establishing a valuable benchmark for ground control station software designed for the efficient coordination of multiple UAVs. A supplemental animated simulation of this work is available at this https URL.
Abstract (translated)
无人机灯光表演（DLShows）作为集群机器人技术快速发展的应用领域，通过数百乃至数千架无人机的协同飞行，打造出令人惊叹的空中视觉盛宴，并成为传统烟花表演的环保可重复替代方案。该领域面临的核心挑战在于：如何在大规模集群中为无人机最优分配视觉航点，并生成平滑无碰撞的飞行轨迹。本文提出的统一分配与轨迹生成（UATG）框架，创新性地同时解决了两个关键问题：无人机到目标位置的最优分配，以及生成动态可行、无碰撞且时间参数化的轨迹。该框架专为DLShows设计，确保编队间转换时间最短，并严格保证无人机间防碰撞。其核心突破在于卓越的计算效率，可实现大规模集群的实时协调——例如，在普通笔记本电脑上约一秒内即可完成1008架无人机的分配与轨迹规划。通过真实环境下的海量仿真验证，该框架展现出编排复杂编队（从字母数字到立体三维造型）的精准性与视觉流畅性。本研究为DLShow产业提供了关键性突破，既给出了生成复杂空中编舞的实用可扩展方案，也为多无人机高效协同的地面控制站软件建立了重要基准。本文配套的动画仿真视频可通过此链接查看。
URL
https://arxiv.org/abs/2603.24401
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AeroScene: Progressive Scene Synthesis for Aerial Robotics
2026-03-24 13:59:45Nghia Vu, Tuong Do, Dzung Tran, Binh X. Nguyen, Hoan Nguyen, Erman Tjiputra, Quang D. Tran, Hai-Nguyen Nguyen, Anh NguyenAbstract
Generative models have shown substantial impact across multiple domains, their potential for scene synthesis remains underexplored in robotics. This gap is more evident in drone simulators, where simulation environments still rely heavily on manual efforts, which are time-consuming to create and difficult to scale. In this work, we introduce AeroScene, a hierarchical diffusion model for progressive 3D scene synthesis. Our approach leverages hierarchy-aware tokenization and multi-branch feature extraction to reason across both global layouts and local details, ensuring physical plausibility and semantic consistency. This makes AeroScene particularly suited for generating realistic scenes for aerial robotics tasks such as navigation, landing, and perching. We demonstrate its effectiveness through extensive experiments on our newly collected dataset and a public benchmark, showing that AeroScene significantly outperforms prior methods. Furthermore, we use AeroScene to generate a large-scale dataset of over 1,000 physics-ready, high fidelity 3D scenes that can be directly integrated into NVIDIA Isaac Sim. Finally, we illustrate the utility of these generated environments on downstream drone navigation tasks. Our code and dataset are publicly available at this http URL
Abstract (translated)
生成模型已在多个领域展现出显著影响，但其在机器人领域的场景合成潜力尚未得到充分探索。这一差距在无人机模拟器中尤为明显——现有仿真环境仍高度依赖手动创建，不仅耗时且难以扩展。本研究提出AeroScene，一种用于渐进式3D场景合成的分层扩散模型。该方法通过层级感知标记化及多分支特征提取，同时推理全局布局与局部细节，确保物理合理性与语义一致性。这使得AeroScene特别适用于生成导航、着陆与栖息等空中机器人任务的真实场景。我们在新收集的数据集及公开基准上开展大量实验，证明AeroScene显著优于现有方法。此外，我们利用该模型生成了包含1000余个物理就绪、高保真3D场景的大规模数据集，可直接集成至NVIDIA Isaac Sim。最后，我们展示了这些生成环境在下游无人机导航任务中的实用性。代码与数据集已公开于此链接。
URL
https://arxiv.org/abs/2603.23224
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Concept-based explanations of Segmentation and Detection models in Natural Disaster Management
2026-03-24 10:03:36Samar Heydari, Jawher Said, Galip \"Umit Yolcu, Evgenii Kortukov, Elena Golimblevskaia, Evgenios Vlachos, Vasileios Mygdalis, Ioannis Pitas, Sebastian Lapuschkin, Leila ArrasAbstract
Deep learning models for flood and wildfire segmentation and object detection enable precise, real-time disaster localization when deployed on embedded drone platforms. However, in natural disaster management, the lack of transparency in their decision-making process hinders human trust required for emergency response. To address this, we present an explainability framework for understanding flood segmentation and car detection predictions on the widely used PIDNet and YOLO architectures. More specifically, we introduce a novel redistribution strategy that extends Layer-wise Relevance Propagation (LRP) explanations for sigmoid-gated element-wise fusion layers. This extension allows LRP relevances to flow through the fusion modules of PIDNet, covering the entire computation graph back to the input image. Furthermore, we apply Prototypical Concept-based Explanations (PCX) to provide both local and global explanations at the concept level, revealing which learned features drive the segmentation and detection of specific disaster semantic classes. Experiments on a publicly available flood dataset show that our framework provides reliable and interpretable explanations while maintaining near real-time inference capabilities, rendering it suitable for deployment on resource-constrained platforms, such as Unmanned Aerial Vehicles (UAVs).
Abstract (translated)
用于洪水和野火分割及目标检测的深度学习模型在嵌入式无人机平台上部署时，能够实现精准、实时的灾害定位。然而，在自然灾害管理中，其决策过程缺乏透明度，阻碍了应急响应所需的人类信任。为解决这一问题，我们提出了一个可解释性框架，用于理解广泛使用的PIDNet和YOLO架构上的洪水分割和车辆检测预测。更具体地说，我们引入了一种新颖的再分配策略，该策略扩展了逐层相关性传播（LRP）对sigmoid门控逐元素融合层的解释。这一扩展使得LRP相关性能够流经PIDNet的融合模块，覆盖整个计算图直至输入图像。此外，我们应用基于原型的概念解释（PCX）在概念层面提供局部和全局解释，揭示哪些学习特征驱动了特定灾害语义类的分割与检测。在公开洪水数据集上的实验表明，我们的框架在保持近实时推理能力的同时，提供了可靠且可解释的解释，使其适用于无人机等资源受限平台的部署。
URL
https://arxiv.org/abs/2603.23020
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UAV-DETR: DETR for Anti-Drone Target Detection
2026-03-24 06:29:51Jun Yang, Dong Wang, Hongxu Yin, Hongpeng Li, Jianxiong YuAbstract
Drone detection is pivotal in numerous security and counter-UAV applications. However, existing deep learning-based methods typically struggle to balance robust feature representation with computational efficiency. This challenge is particularly acute when detecting miniature drones against complex backgrounds under severe environmental interference. To address these issues, we introduce UAV-DETR, a novel framework that integrates a small-target-friendly architecture with real-time detection capabilities. Specifically, UAV-DETR features a WTConv-enhanced backbone and a Sliding Window Self-Attention (SWSA-IFI) encoder, capturing the high-frequency structural details of tiny targets while drastically reducing parameter overhead. Furthermore, we propose an Efficient Cross-Scale Feature Recalibration and Fusion Network (ECFRFN) to suppress background noise and aggregate multi-scale semantics. To further enhance accuracy, UAV-DETR incorporates a hybrid Inner-CIoU and NWD loss strategy, mitigating the extreme sensitivity of standard IoU metrics to minor positional deviations in small objects. Extensive experiments demonstrate that UAV-DETR significantly outperforms the baseline RT-DETR on our custom UAV dataset (+6.61% in mAP50:95, with a 39.8% reduction in parameters) and the public DUT-ANTI-UAV benchmark (+1.4% in Precision, +1.0% in F1-Score). These results establish UAV-DETR as a superior trade-off between efficiency and precision in counter-UAV object detection. The code is available at this https URL.
Abstract (translated)
无人机检测在众多安全与反无人机应用中至关重要。然而，现有的基于深度学习的方法通常难以在鲁棒的特征表示与计算效率之间取得平衡。这一挑战在复杂背景与严重环境干扰下检测微型无人机时尤为突出。为应对这些问题，我们提出了UAV-DETR，一种集成了小目标友好架构与实时检测能力的新型框架。具体而言，UAV-DETR采用WTConv增强的骨干网络与滑动窗口自注意力（SWSA-IFI）编码器，在捕获微小目标高频结构细节的同时大幅减少参数开销。此外，我们提出了高效跨尺度特征重校准与融合网络（ECFRFN）以抑制背景噪声并聚合多尺度语义信息。为进一步提升精度，UAV-DETR结合了混合Inner-CIoU与NWD损失策略，缓解了标准IoU指标对小目标微小位置偏差的极端敏感性。大量实验表明，UAV-DETR在我们自建的无人机数据集上显著优于基线模型RT-DETR（mAP50:95提升6.61%，参数量减少39.8%），并在公开的DUT-ANTI-UAV基准上实现精度提升1.4%、F1分数提升1.0%。这些结果确立了UAV-DETR在反无人机目标检测中效率与精度之间的优异权衡。代码可通过此https链接获取。
URL
https://arxiv.org/abs/2603.22841
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Static Scene Reconstruction from Dynamic Egocentric Videos
2026-03-23 18:19:46Qifei Cui, Patrick ChenAbstract
Egocentric videos present unique challenges for 3D reconstruction due to rapid camera motion and frequent dynamic interactions. State-of-the-art static reconstruction systems, such as MapAnything, often degrade in these settings, suffering from catastrophic trajectory drift and "ghost" geometry caused by moving hands. We bridge this gap by proposing a robust pipeline that adapts static reconstruction backbones to long-form egocentric video. Our approach introduces a mask-aware reconstruction mechanism that explicitly suppresses dynamic foreground in the attention layers, preventing hand artifacts from contaminating the static map. Furthermore, we employ a chunked reconstruction strategy with pose-graph stitching to ensure global consistency and eliminate long-term drift. Experiments on HD-EPIC and indoor drone datasets demonstrate that our pipeline significantly improves absolute trajectory error and yields visually clean static geometry compared to naive baselines, effectively extending the capability of foundation models to dynamic first-person scenes.
Abstract (translated)
第一人称视角视频因相机快速运动和频繁的动态交互，给三维重建带来了独特挑战。最先进的静态重建系统（如MapAnything）在此类场景中常因灾难性轨迹漂移和移动双手产生的“残影”几何体而性能下降。我们通过提出一种鲁棒的流程来弥合这一差距，该流程将静态重建主干网络适配于长视频形式的第一人称视角视频。我们的方法引入了一种掩码感知重建机制，该机制在注意力层中显式抑制动态前景，防止手部伪影污染静态地图。此外，我们采用分块重建策略并结合位姿图拼接，以确保全局一致性并消除长期漂移。在HD-EPIC和室内无人机数据集上的实验表明，与朴素基线相比，我们的流程显著改善了绝对轨迹误差，并生成了视觉上更干净的静态几何体，有效将基础模型的能力扩展到了动态第一人称场景。
URL
https://arxiv.org/abs/2603.22450
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No Dense Tensors Needed: Fully Sparse Object Detection on Event-Camera Voxel Grids
2026-03-23 07:12:14Mohamad Yazan Sadoun, Sarah Sharif, Yaser Mike BanadAbstract
Event cameras produce asynchronous, high-dynamic-range streams well suited for detecting small, fast-moving drones, yet most event-based detectors convert the sparse event stream into dense tensors, discarding the representational efficiency of neuromorphic sensing. We propose SparseVoxelDet, to our knowledge the first fully sparse object detector for event cameras, in which backbone feature extraction, feature pyramid fusion, and the detection head all operate exclusively on occupied voxel positions through 3D sparse convolutions; no dense feature tensor is instantiated at any stage of the pipeline. On the FRED benchmark (629,832 annotated frames), SparseVoxelDet achieves 83.38% mAP at 50 while processing only 14,900 active voxels per frame (0.23% of the T.H.W grid), compared to 409,600 pixels for the dense YOLOv11 baseline (87.68% mAP at 50). Relaxing the IoU threshold from 0.50 to 0.40 recovers mAP to 89.26%, indicating that the remaining accuracy gap is dominated by box regression precision rather than detection capability. The sparse representation yields 858 times GPU memory compression and 3,670 times storage reduction relative to the equivalent dense 3D voxel tensor, with data-structure size that scales with scene dynamics rather than sensor resolution. Error forensics across 119,459 test frames confirms that 71 percent of failures are localization near-misses rather than missed targets. These results demonstrate that native sparse processing is a viable paradigm for event-camera object detection, exploiting the structural sparsity of neuromorphic sensor data without requiring neuromorphic computing hardware, and providing a framework whose representation cost is governed by scene activity rather than pixel count, a property that becomes increasingly valuable as event cameras scale to higher resolutions.
Abstract (translated)
事件相机产生的异步高动态范围流非常适用于检测小型快速移动的无人机，但大多数基于事件的检测器会将稀疏事件流转换为稠密张量，从而丢弃了神经形态传感的表征效率。我们提出了SparseVoxelDet，据我们所知，这是首个面向事件相机的全稀疏目标检测器，其骨干特征提取、特征金字塔融合和检测头均通过3D稀疏卷积仅在占据体素位置上进行运算；管道中任何阶段都不会实例化稠密特征张量。在FRED基准测试（629,832个标注帧）上，SparseVoxelDet在每帧仅处理14,900个活跃体素（占T.H.W网格的0.23%）的情况下，实现了50%交并比下83.38%的平均精度，而稠密YOLOv11基线（50%交并比下87.68%的平均精度）需处理409,600个像素。将交并比阈值从0.50放宽至0.40后，平均精度恢复至89.26%，表明剩余的精度差距主要源于边界框回归精度而非检测能力。稀疏表征相较于等效的稠密3D体素张量，实现了GPU内存压缩858倍与存储缩减3,670倍，且数据结构尺寸随场景动态变化而非传感器分辨率增长。对119,459个测试帧的错误溯源分析证实，71%的失败案例源于定位偏差而非漏检目标。这些结果表明，原生稀疏处理是事件相机目标检测的可行范式，它无需神经形态计算硬件即可利用神经形态传感器数据的结构稀疏性，并提供了一种表示成本由场景活动度而非像素数量决定的框架——这一特性在事件相机向更高分辨率发展时将愈发重要。
URL
https://arxiv.org/abs/2603.21638
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