Semantic segmentation method and device, electronic equipment and computer readable storage medium

Abstract

The embodiment of the invention provides a semantic segmentation method and device, electronic equipment and a computer readable storage medium. The method comprises: obtaining a to-be-processed image; and performing semantic segmentation processing on the to-be-processed image by adopting the semantic segmentation model to obtain a semantic segmentation result of the to-be-processed image, wherein the semantic segmentation model is obtained by taking a first transformation feature, which is obtained by performing contour decomposition or enhancement processing on a first intermediate feature output by a reference semantic model, as a reference, and combining with a second transformation feature, which is obtained by performing contour decomposition or enhancement processing on a second intermediate feature output by the to-be-trained semantic segmentation model. The first intermediate feature and the second intermediate feature comprise at least one of the following groups: a first texture feature and a second texture feature; and a first semantic feature and a second semantic feature. The first transformation feature and the second transformation feature comprise at least one of the following groups: a first contour feature and a second contour feature; and a first enhancement feature and a second enhancement feature.

Description

technical field [0001] The present disclosure relates to image processing technology, and in particular to a semantic segmentation method, device, electronic equipment and computer-readable storage medium. Background technique [0002] With the development of semantic segmentation technology, knowledge distillation is introduced into semantic segmentation technology; knowledge distillation can transfer the knowledge learned from complex models to simple models, so that in practical applications, simple models can be conveniently used for semantic segmentation; however, In the process of knowledge transfer, the result of semantic segmentation of the complex model is usually used as the response-based knowledge to guide the learning of the simple model. In this way, the knowledge transferred to the simple model is not rich enough, resulting in the failure of the semantic segmentation of the simple model after learning. Low precision. Contents of the invention [0003] Embod...

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Problems solved by technology

[0002] With the development of semantic segmentation technology, knowledge distillation is introduced into semantic segmentation technology; knowledge distillation can transfer the knowledge learned from complex models to simple models, so that in practical applications, simple models can be conveniently used for semantic segmentation; however, In the process of knowledge transfer, the result of semantic segmentation of the complex model is usually used as the response-based knowledge to guide the learning of the simple model. In this way, the knowledge transferred to the simple model is not rich enough, resulting in the failure of the semantic segmentation of the simple model after learning. low precision

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