Methods for performing self-supervised learning of deep-learning based detection network by using deep q-network and devices using the same

Abstract

A method of self-supervised learning for detection network using deep Q-network includes steps of: performing object detection on first unlabeled image through the detection network trained with training database to generate first object detection information and performing learning operation on a first state set corresponding to the first object detection information to generate a Q-value, if an action of the Q-value accepts the first unlabeled image, testing the detection network, retrained with the training database additionally containing a labeled image of the first unlabeled image, to generate a first accuracy, and if the action rejects the first unlabeled image, testing the detection network without retraining, to generate a second accuracy, and storing the first state set, the action, a reward of the first or the second accuracy, and a second state set of a second unlabeled image as transition vector, and training the deep Q-network by using the transition vector.