A method and device for sample augmentation for target key point detection

Abstract

The present application adopts a sample augmentation method for target key point detection. First, the original sample for target key point detection is obtained. The original sample is composed of an image and label information of target key points corresponding to the image. The image of the original sample and the labeling information of the target key points and the parameters of the augmentation operation, determine the gray value of the pixels of the image of the augmented sample and the marked target key points in the image of the original sample in the augmented sample The corresponding cell value coordinates in the image. The image of the augmented sample and the image of the original sample can be semantically aligned by this method, and high-precision sample augmentation can be realized. It is especially suitable for the algorithm of object key point detection class which is extremely sensitive to augmented accuracy.

Description

technical field [0001] The present application relates to the technical field of computer image processing, and in particular to a sample augmentation technique for object key point detection. Background technique [0002] In the field of computer image processing technology, a series of similar but different training samples are obtained through sample augmentation operations, that is, by randomly changing the acquired samples, thereby expanding the diversity of the training data set. Especially for the neural network model based on deep learning, the augmentation operation can reduce the dependence of the neural network model on certain attributes, thereby improving the generalization ability of the neural network model. [0003] During the operation of the existing sample augmentation methods, due to the irrationality of the algorithm design, the augmented sample and the original sample are not semantically aligned. The error caused by this sample augmentation will affec...

AI Technical Summary

Problems solved by technology

[0003] During the operation of the existing sample augmentation method, due to the irrationality of the algorithm design, the augmented sample and the original sample are not aligned at the semantic level

The error caused by this sample augmentation will affect the convergence speed of the neural network model training process, and will also affect the performance of the neural network model during the test process, especially the target key point detection algorithm that is extremely sensitive to augmented accuracy. neural network model

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