Image training sample generation method and device

Abstract

The invention discloses an image training sample generation method and device, and belongs to the field of computer vision. According to the method, the model transformation is carried out on the obtained three-dimensional texture model of the target object to obtain a plurality of three-dimensional transformation models, and then one or more two-dimensional images corresponding to each three-dimensional transformation model are generated, so that a large number of image training samples similar to the target object can be obtained. That is to say, the scheme can quickly and automatically generate a large number of image training samples meeting requirements, and manpower and time costs are saved. Moreover, because the image training samples in the scheme are obtained according to the three-dimensional texture model of the target object, the image training samples and the target object belong to the same type, and the fidelity is high. Thus, compared with an image training sample captured from the Internet, the sample processing process can be simplified, the problem that the quality of the sample is reduced due to manual operation is solved, and subsequent training of the deep learning network is facilitated.

Description

technical field [0001] The present application relates to the field of computer vision (Computer Vision, CV), and in particular to a method and device for generating image training samples. Background technique [0002] At present, deep learning technology is widely used in CV field. In order to enhance the generalization ability of deep learning network and reduce the overfitting of deep learning network, it is usually necessary to use massive image training samples to train deep learning network. How to obtain massive Image training samples have become an urgent problem to be solved in the application of deep learning technology in the field of CV. [0003] In related technologies, image training samples may be obtained by collecting images with a camera or grabbing images from the Internet. However, because the method of camera acquisition is limited by hardware, labor costs, etc., it takes a long time to acquire images of sufficient magnitude. However, most of the imag...

AI Technical Summary

Problems solved by technology

However, because the method of camera acquisition is limited by hardware, labor costs, etc., it takes a long time to acquire images of sufficient magnitude

However, most of the images captured from the Internet cannot be used directly, and it is necessary to rely on manual cleaning and labeling of the captured images for sample enhancement. The time and labor costs are also high, and manual operations may lead to a decline in sample quality. , affecting the training of deep learning networks

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