Distributed and Efficient Object Detection via Interactions Among Devices, Edge, and Cloud

With the rapid development of Internet-of-Things and communication techniques, media transmission in surveillance applications is gradually relying on wireless networks. Meanwhile, the emergence of edge computing has pushed the media data analysis from the cloud to the edge of the network to achieve fast response for delay-sensitive media processing tasks. Object detection is a representative delay-sensitive image processing task in surveillance applications, but faces significant challenges in this context. For example, how to compress images for transmission in wireless environment without compromising the detection accuracy, and how to integrate and update local inference models online in an edge computing-based object detection system. In this paper, we propose an object detection architecture based on edge computing to achieve distributed and efficient object detection for surveillance applications. Under this architecture, we develop an adaptive Region-of-Interest-based image compression scheme for end devices to efficiently compress their captured images for wireless transmission but not to sacrifice the object detection accuracy of edge servers. Furthermore, we carefully design distributed and communication-efficient interactions among end devices, edge servers, and the cloud to dynamically optimize the object detection accuracy online. Extensive simulation results demonstrate that our proposed architecture not only achieves a competitive detection accuracy to traditional cloud-based objective detection solution with reduced response delay but also significantly improves the image transmission efficiency with adaptive image compression ratio.