Two-dimensional code recognition method

Abstract

The invention discloses a two-dimensional code recognition method, and the method comprises the steps: selecting QR two-dimensional codes in different backgrounds according to the actual recognition demands, adding motion blur with different blur lengths and blur angles, and designing and manufacturing a two-dimensional code data set based on motion blur; secondly, an image motion blur removing algorithm based on a generative adversarial network is adopted; according to the image deblurring method based on the generative adversarial network, the feature pyramid-based image feature extraction network is adopted to extract feature mapping of the image, and a new GAN loss function is designed, so that the image deblurring algorithm based on the generative adversarial network is faster in training speed and higher in robustness, and a very efficient image deblurring effect can be provided. An image binarization algorithm based on an adaptive threshold and a series of algorithms based on image processing are designed, so that an image edge region is better extracted and an image is better corrected. The method can be applied to two-dimensional code recognition environments with different backgrounds.

Description

technical field [0001] The invention relates to the field of robot image processing, in particular to a two-dimensional code recognition method. Background technique [0002] In recent years, with the rapid development of science and technology, two-dimensional codes, as one of the emerging automatic identification technologies, are more and more used in Many industries and fields, such as shared bicycles, mobile payment, warehouse scheduling, robot positioning and navigation, and social software, have greatly facilitated people's daily life. [0003] In recent years, thanks to the rapid development of deep learning, researchers have proposed many image processing algorithms based on convolutional neural networks. These algorithms complete the given tasks through iterative training for large-scale training samples. Compared with traditional algorithms, algorithms based on convolutional neural networks have shown better performance and higher robustness, so they quickly form...

AI Technical Summary

Problems solved by technology

[0006] In view of the situation that the two-dimensional code image cannot be accurately recognized due to motion blur, a new method of two-dimensional code recognition based on the combination of the de-motion blur algorithm based on the generative confrontation network and the traditional image processing alg...

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