Distributing Intelligence for Object Detection Using Edge Computing

Object detection plays an important role in many artificial intelligence applications such as autonomous driving and video surveillance. However, running object detection models on small edge devices remains computationally expensive and time consuming. In this paper, we present a distributed cloud-edge version of the YOLOv3 model based on split learning. By keeping the data local and sharing only part of the model, both computational and privacy requirements were met. The definition of the cut layer was set by means of a comprehensive analysis of the model architecture. Validation of correctness was established using the COCO dataset and performance comparison was made with client-server basic distribution and federated learning.