Robot target recognition using deep federated learning

Abstract

Robot target recognition is a critical and fundamental machine vision task. In this paper, InVision, a robot target recognition approach is proposed using deep federated learning. Particularly, deep geometric learning is developed to improve the perception capabilities of convolutional neural networks, and promote the representation maps' resolutions while achieving good recognition performance. Moreover, federated metric learning is constructed to protect user data privacy across multiple devices and relieve the problem of inadequate available labeled training data. To improve the speed of the recognition system, a lightweight deep neural network is presented. Extensive experiments are performed, showing that InVision significantly outperforms the outstanding comparison approaches.