中图分类号：TP391.41 文献标志码：A DOI: 10.16157/j.issn.0258-7998.267951
中文引用格式： 李家鑫，王韵涵，潘果，等. Lite-VAFNet：面向无人机边缘计算的多模态3D目标检测[J]. 电子技术应用，2026，52(4)：10-17.
英文引用格式： Li Jiaxin，Wang Yunhan，Pan Guo，et al. Lite-VAFNet: efficient multi-modal 3D object detection for UAV edge computing[J]. Application of Electronic Technique，2026，52(4)：10-17.

Lite-VAFNet： efficient multi-modal 3D object detection for UAV edge computing

Abstract： To address the conflict between the limited edge computing capacity of Unmanned Aerial Vehicles （UAVs） and the demand for high-precision multi-modal 3D detection， this paper proposes a lightweight detection network tailored for airborne platforms， termed Lite-VAFNet， building upon VAF-Net. A grid dimensionality-reduction detection head is constructed to compress the parameter volume by approximately 63%， thereby alleviating device storage constraints. A Linear-Bottleneck fusion module is designed to execute feature interaction with linear complexity， effectively eliminating the peak memory bottleneck. Furthermore， a collaborative acceleration framework integrating spatial resampling and logit distillation is introduced to overcome memory access limitations while compensating for the accuracy degradation induced by quantization. Experiments on the KITTI benchmark demonstrate that Lite-VAFNet achieves a 3D AP （Mod.） of 85.24% with merely 14.60 M parameters， significantly outperforming state-of-the-art models such as BEVFusion. This research strikes an optimal balance between accuracy and efficiency while substantially reducing resource consumption， exhibiting exceptional potential for edge deployment.

Key words : drone perception；multimodal 3D object detection；model lightweighting；knowledge distillation；edge computing