A Method for Image Semantic Segmentation

Abstract

The invention discloses an image semantic segmentation method, which solves the problem of over-reliance on the performance of the image translation model by the image semantic segmentation of the synthesized data set by reversely improving the image translation model through the first image segmentation model, through the optimized image translation model (The output data of the first image translation model) and the output data of the first image segmentation model, so that the first image segmentation model can be optimized again through supervised learning to obtain an image semantic segmentation with stronger domain-invariant feature discrimination model (the second image segmentation model), using the image semantic segmentation model (the second image segmentation model) to carry out semantic segmentation on the target image, and then the image semantic segmentation can be completed. The method solves the problem in the prior art that the performance of the image translation model has too much influence on the segmentation result and cannot be corrected.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image semantic segmentation method. Background technique [0002] The pixel-level semantic segmentation map is to mark the pixels belonging to different categories on the picture with different labels, and it has the function of eyes in automatic driving. The vehicle takes a picture of the front, obtains the segmentation map through the segmentation model, and obtains the category and location information contained in the road. This information is fed back to the automatic driving system to determine whether to move forward, stop, turn, or other operations. [0003] The main difficulty faced by image segmentation techniques on synthetic datasets is that when artificial labels from the target domain are not used at all, the segmentation model trained from synthetic datasets will experience domain shift in application. Therefore, the main breakthrough point of the existing synth...

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Problems solved by technology

[0006] Aiming at the above deficiencies in the prior art, an image semantic segmentation method provided by the present invention solves the problem in the prior art that the performance of the image translation model has too much influence on the segmentation result and cannot be corrected, and provides a method with stronger Image Semantic Segmentation Based on Domain-Invariant Feature Discrimination

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