Methods for training auto labeling device and performing auto labeling related to segmentation while performing automatic verification by using uncertainty scores and devices using the same

Abstract

A method of training an auto labeling device is provided. The method includes steps of: a learning device (a) inputting first images into an FPN to generate first pyramid feature maps, instructing an object detection network (ODN) to generate first bounding boxes, an ROI pooling layer to generate first pooled feature maps, and a deconvolution network to generate first segmentation masks, and training the ODN, the FPN, and the deconvolution network and (b) inputting second images into the FPN to generate second pyramid feature maps, and instructing the ODN to generate second bounding boxes, the ROI pooling layer to generate second pooled feature maps, and the deconvolution network to generate second segmentation masks, and inputting the second pooled feature maps into at least one of first and second classifiers to generate per-pixel class scores and mask uncertainty scores, and training one of the first and the second classifiers.