KeepEdge: A Knowledge Distillation Empowered Edge Intelligence Framework for Visual Assisted Positioning in UAV Delivery
Haoyu Luo, Tianxiang Chen, Xuejun Li, Shuangyin Li, Chong Zhang, Gansen Zhao, Xiao Liu
Abstract
The Unmanned Aerial Vehicles (UAVs) delivery service is being increasingly used in logistics. However, it is challenging for a UAV to precisely identify the position for parcel delivering if it is only aided by the GPS, especially in some complex environments with weak signals and high interference. For this issue, we present a knowledge distillation empowered edge intelligence architecture, KeepEdge, to achieve visual information-assisted positioning for the UAV delivery services. Specifically, we integrate deep neural networks (DNN) into an edge computing framework to enable edge intelligence which empowers the UAVs to autonomously identify the expected delivery position. Deploying the DNN model and conducting model inference on UAVs however, requires high computing performance. To manage the trade-off between the limited resources onboard the UAVs and high-performance requirements, we employ knowledge distillation to produce a lightweight model with high accuracy based on the full model trained in the cloud. The lightweight model with significantly lower complexity and less inference latency is used onboard of the UAVs for accurate positioning. Comprehensive experiments show that the proposed architecture achieves satisfactory performance for assisted positioning. A real-world case study is presented to demonstrate the effectiveness of the proposed edge intelligence solution for UAV delivery services.