# Leveraging Large Vision Model for Multi-UAV Co-perception in Low-Altitude Wireless Networks Source: [Leveraging Large Vision Model for Multi-UAV Co-perception in Low-Altitude Wireless Networks]() Collection index: [llms.txt]() Data updated: 2026-03-20T05:00:04.947Z Authors: Yunting Xu, Jiacheng Wang, Ruichen Zhang, Changyuan Zhao, Yinqiu Liu, Dusit Niyato, Liang Yu, Haibo Zhou, Dong In Kim Published: 2026-03-13T17:49:28.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Multi-UAV Collaborative Panoptic Segmentation on Air-Co-Pred (test)](): Multi-UAV Collaborative Panoptic Segmentation; Air-Co-Pred (test); IoU (Short): 65.52. - [Cooperative Perception on Air-Co-Pred Short Long Avg](): Cooperative Perception; Air-Co-Pred Short Long Avg; IoU (Short): 65.71. ## Original sources and implementation [Original paper]() [PDF]()