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

TL;DR

This paper introduces a communication-efficient multi-UAV perception framework that reduces data transmission while improving perception accuracy using selective visual data sharing, advanced encoding, and reinforcement learning.

Contribution

It proposes a novel BHU framework combining pixel selection, MIMO transmission, and deep reinforcement learning for optimized UAV cooperation in low-altitude wireless networks.

Findings

Perception performance improved by over 5% compared to CNN baselines.

Communication overhead reduced by 85%.

Effective balance between data efficiency and perception accuracy achieved.

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.