Method and device for automatically learning network preprocessing parameters, electronic equipment and storage medium

Abstract

The invention discloses a method and device for automatically learning network preprocessing parameters, electronic equipment and a storage medium, and the method comprises the steps: carrying out the training of a semantic segmentation model through randomly constructed image processing parameter items, and enabling the image processing parameter items to be used for adjusting the display effect of each picture, the image processing parameter item comprises an image processing parameter value; inputting the plurality of pictures into a preset parameter preprocessing network to obtain an image processing target parameter value corresponding to each picture; according to the image processing target parameter value corresponding to each picture, adjusting the picture to be subjected to semantic segmentation processing to obtain a plurality of target pictures; and inputting the plurality of target pictures into the semantic segmentation model obtained by pre-training, and carrying out image semantic segmentation. According to the invention, the semantic segmentation effect of the semantic segmentation model during prediction is improved.

Description

technical field [0001] The present application relates to the technical field of computer vision, and in particular, to a method, apparatus, electronic device, and storage medium for automatically learning network preprocessing parameters. Background technique [0002] Semantic segmentation, the process of linking every pixel in an image to a class label, is very important in self-driving car technology because it is important for a model to understand the context in which it operates. [0003] In the related art, when using a trained semantic segmentation model for prediction, the data to be input into the model is not transformed into brightness, contrast, saturation, etc. corresponding to the training, but the data is directly input to the network Make predictions, resulting in a poor segmentation effect of the semantic segmentation model. SUMMARY OF THE INVENTION [0004] The embodiments of the present application provide a method, an apparatus, an electronic device, ...

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Problems solved by technology

[0003] In related technologies, when the trained semantic segmentation model is used for prediction, the data to be input into the model is not transformed into brightness, contrast, saturation, etc. corresponding to the training, but the data is directly input to the network. Prediction, resulting in poor segmentation effect of semantic segmentation model

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