Leveraging Large Vision Model for Multi-UAV Co-perception in Low-Altitude Wireless Networks

• URL: http://arxiv.org/abs/2603.16927v1
• Date: Fri, 13 Mar 2026 17:49:28 GMT
• Title: Leveraging Large Vision Model for Multi-UAV Co-perception in Low-Altitude Wireless Networks
• Abstract summary: Multi-uncrewed aerial vehicles (UAV) cooperative perception has emerged as a promising paradigm for diverse low-altitude economy applications.<n>This paper proposes a communication-efficient cooperative perception framework, termed Base-Station-Helped UAV (BHU)<n>We employ a Top-K selection mechanism to identify the most informative pixels from UAV-captured RGB images, enabling sparsified visual transmission with reduced data volume and latency.
• Score: 51.323657802729535
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Multi-uncrewed aerial vehicle (UAV) cooperative perception has emerged as a promising paradigm for diverse low-altitude economy applications, where complementary multi-view observations are leveraged to enhance perception performance via wireless communications. However, the massive visual data generated by multiple UAVs poses significant challenges in terms of communication latency and resource efficiency. To address these challenges, this paper proposes a communication-efficient cooperative perception framework, termed Base-Station-Helped UAV (BHU), which reduces communication overhead while enhancing perception performance. Specifically, we employ a Top-K selection mechanism to identify the most informative pixels from UAV-captured RGB images, enabling sparsified visual transmission with reduced data volume and latency. The sparsified images are transmitted to a ground server via multi-user MIMO (MU-MIMO), where a Swin-large-based MaskDINO encoder extracts bird's-eye-view (BEV) features and performs cooperative feature fusion for ground vehicle perception. Furthermore, we develop a diffusion model-based deep reinforcement learning (DRL) algorithm to jointly select cooperative UAVs, sparsification ratios, and precoding matrices, achieving a balance between communication efficiency and perception utility. Simulation results on the Air-Co-Pred dataset demonstrate that, compared with traditional CNN-based BEV fusion baselines, the proposed BHU framework improves perception performance by over 5% while reducing communication overhead by 85%, providing an effective solution for multi-UAV cooperative perception under resource-constrained wireless environments.

Related papers

• Spatio-Temporal Attention Enhanced Multi-Agent DRL for UAV-Assisted Wireless Networks with Limited Communications [73.26813382994867]
  We propose a delay-tolerant multi-agent deep reinforcement learning algorithm that integrates a delay-penalized reward to encourage information sharing among UAVs.<n>We show that our new approach achieves over 50% reduction in information delay and 75% throughput gain compared to the conventional MADRL.<n>It is also effective in reducing the need for UAVs' information exchange and thus fostering practical deployment of MADRL in UAV-assisted wireless networks.
  arXiv  Detail & Related papers  (2026-03-23T05:42:50Z)
• When UAV Swarm Meets IRS: Collaborative Secure Communications in Low-altitude Wireless Networks [68.45202147860537]
  Low-altitude wireless networks (LAWNs) provide enhanced coverage, reliability, and throughput for diverse applications.<n>These networks face significant security vulnerabilities from both known and potential unknown eavesdroppers.<n>We propose a novel secure communication framework for LAWNs where the selected UAVs within a swarm function as a virtual antenna array.
  arXiv  Detail & Related papers  (2025-10-25T02:02:14Z)
• Efficient Onboard Vision-Language Inference in UAV-Enabled Low-Altitude Economy Networks via LLM-Enhanced Optimization [61.55616421408666]
  Low-Altitude Economy Networks (LAENets) have enabled a variety of applications, including aerial surveillance, environmental sensing, and semantic data collection.<n> onboard vision (VLMs) offer inference for real-time inference but limited onboard dynamic network conditions.<n>We propose a UAV-enabled LAENet system that improves communication efficiency under dynamic LAENet conditions.
  arXiv  Detail & Related papers  (2025-10-11T05:11:21Z)
• Refine-and-Contrast: Adaptive Instance-Aware BEV Representations for Multi-UAV Collaborative Object Detection [15.494912154439367]
  Multi-UAV collaborative 3D detection enables accurate and robust perception by fusing multi-view observations from aerial platforms.<n>We present AdaBEV, a novel framework that learns adaptive instance-aware BEV representations through a refine-and-contrast paradigm.
  arXiv  Detail & Related papers  (2025-08-18T07:37:14Z)
• Is Intermediate Fusion All You Need for UAV-based Collaborative Perception? [1.8689461238197957]
  We propose a novel communication-efficient collaborative perception framework based on late-intermediate fusion, dubbed LIF.<n>We leverage vision-guided positional embedding (VPE) and box-based virtual augmented feature (BoBEV) to effectively integrate complementary information from various agents.<n> Experimental results demonstrate that our LIF achieves superior performance with minimal communication bandwidth, proving its effectiveness and practicality.
  arXiv  Detail & Related papers  (2025-04-30T16:22:14Z)
• UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in Data Collection Networks [71.23793087286703]
  Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications.<n>This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems.
  arXiv  Detail & Related papers  (2024-12-09T12:56:50Z)
• IFTR: An Instance-Level Fusion Transformer for Visual Collaborative Perception [9.117534139771738]
  Multi-agent collaborative perception has emerged as a widely recognized technology in the field of autonomous driving. Current collaborative perception predominantly relies on LiDAR point clouds, with significantly less attention given to methods using camera images. This work proposes an instance-level fusion transformer for visual collaborative perception.
  arXiv  Detail & Related papers  (2024-07-13T11:38:15Z)
• UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning [79.16150966434299]
  We formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs. We use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB.
  arXiv  Detail & Related papers  (2024-04-11T03:19:22Z)
• Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs [21.195346908715972]
  Unmanned aerial vehicles present an alternative means to offload data traffic from terrestrial BSs. This paper presents a novel approach to efficiently serve multiple UAVs for data offloading from terrestrial BSs.
  arXiv  Detail & Related papers  (2024-02-05T12:36:08Z)
• Graph Attention-based Reinforcement Learning for Trajectory Design and Resource Assignment in Multi-UAV Assisted Communication [20.79743323142469]
  It is challenging for UAV base stations (UAV BSs) to realize trajectory design and resource assignment in unknown environments. The cooperation and competition between UAV BSs in the communication network leads to a Markov game problem. In this paper, a novel graph-attention multi-agent trust region (GA-MATR) reinforcement learning framework is proposed to solve the multi-UAV assisted communication problem.
  arXiv  Detail & Related papers  (2024-01-31T14:37:06Z)
• Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning [52.7230652428711]
  Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection.
  arXiv  Detail & Related papers  (2023-06-05T16:01:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.