Context-Aware Object Detection for Vehicular Networks Based on Edge-Cloud Cooperation

Abstract

Due to high mobility and high dynamic environments, object detection for vehicular networks is one of the most challenging tasks. However, the development of integration techniques, such as software-defined networking (SDN) and network function visualization (NFV), in networking, caching, and computing provides us with new approaches. In this article, we propose a novel context-aware object detection method based on edge-cloud cooperation. Specifically, an object detection model based on deep learning is established in the cloud server. Different from other methods, to further explore the underlying inner spatial features of collected images, the visual objects of images are regarded as nodes and the spatial relations between objects as edges, then a type of message-passing method is employed to update the nodes' features. In the mobile edge computing (MEC) servers, the context information and captured images of the vehicular environments are extracted and then are used to adjust the object detection model from the cloud server. In this way, the cloud server cooperates with the MEC servers to realize context-aware object detection, which improves the adaptation and performance of the detection model under different scenarios. The simulation results also demonstrate that the proposed method is more accurate and faster than the previous methods.