Method and device for generating data set, and testing method and testing device using same

Abstract

A method of generating at least one image data set to be used for learning CNN capable of detecting at least one obstruction in one or more autonomous driving circumstances, comprising steps of: (a) alearning device acquiring (i) an original image representing a road driving circumstance and (ii) a synthesized label obtained by using an original label corresponding to the original image and an additional label corresponding to an arbitrary specific object, wherein the arbitrary specific object does not relate to the original image; and (b) the learning device supporting a first CNN module togenerate a synthesized image using the original image and the synthesized label, wherein the synthesized image is created by combining an image of the arbitrary specific object corresponding to the additional label and the original image.

Description

technical field [0001] The present invention relates to a method and a device for generating a CNN learning data set for detecting at least one obstacle in an automatic driving situation, a testing method and a testing device using the same. Background technique [0002] Deep Convolutional Neural Networks (Deep CNNs) are at the heart of amazing developments in the field of deep learning. CNNs have been used to solve text recognition problems in the 90s, but their widespread use today is due to recent research results. The above-mentioned deep CNN won the championship in the ImageNet image classification competition in 2012. Convolutional neural networks have since become very useful tools in the field of Machine Learning. [0003] In addition, image segmentation (Image segmentation) is a method of receiving an image (training image or test image) as input and creating a label (label) as output. Recently, with the attention of deep learning (Deep learning) technology, imag...

AI Technical Summary

Problems solved by technology

That is, it is difficult to obtain learning images for special objects that do not often appear on the road from general driving image data

For example, it is easier to obtain image data for people, bicycles, and vehicles from general driving image data, so in order to improve detection performance, learning can be performed on images containing these objects, but it is difficult to obtain images for tigers or crocodiles from general driving image data data, so there is a problem that it is not easy to learn to improve the detection performance of special objects

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