KeepEdge: A Knowledge Distillation Empowered Edge Intelligence Framework for Visual Assisted Positioning in UAV Delivery

Abstract

The Unmanned Aerial Vehicles (UAVs) delivery is being increasingly used in the field of logistics. However, it is highly 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. To address this issue, we present a knowledge distillation empowered edge intelligence framework, KeepEdge, to achieve visual information-assisted positioning for the last mile UAV delivery services. In our approach, 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 using visual information. 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, here, 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 framework achieves satisfactory performance for assisted positioning. A real-world case study is also presented to demonstrate the effectiveness of the proposed edge intelligence solution for UAV delivery services.