Method for automatically evaluating labeling reliability of training images for use in deep learning network to analyze images, and reliability-evaluating device using the same

Abstract

A method for evaluating a reliability of labeling training images to be used for learning a deep learning network is provided. The method includes steps of: a reliability-evaluating device instructing a similar-image selection network to select validation image candidates with their own true labels having shooting environments similar to those of acquired original images, which are unlabeled images, and instructing an auto-labeling network to auto-label the validation image candidates with their own true labels and the original images; and (i) evaluating a reliability of the auto-labeling network by referring to true labels and auto labels of easy-validation images, and (ii) evaluating a reliability of a manual-labeling device by referring to true labels and manual labels of difficult-validation images. This method can be used to recognize surroundings by applying a bag-of-words model, to optimize sampling processes for selecting a valid image among similar images, and to reduce annotation costs.