An image semantic segmentation method based on deep learning

Abstract

The invention discloses an image semantic segmentation method based on deep learning. The method comprises four parts of data set processing, deep semantic segmentation network construction, deep semantic segmentation network training and parameter learning, and semantic segmentation on a test image. The RGB image and the gray level image of the input image are used as the input of the network model, the edge information of the gray level image is fully utilized, and the richness degree of input characteristics is effectively increased; a convolutional neural network and a bidirectional threshold recursion unit are combined, and more context dependency relationships and global feature information are captured on the basis of learning image local features; coordinate information is added tothe feature map through the first coordinate channel module and the second coordinate channel module, the coordinate features of the model are enriched, the generalization ability of the model is improved, and a semantic segmentation result with high resolution and accurate boundary is generated.

Description

technical field [0001] The invention belongs to the technical field of computer vision and deep learning, and in particular relates to an image semantic segmentation method based on deep learning. Background technique [0002] Image semantic segmentation is to understand and recognize the content of pictures from the pixel level. Its purpose is to establish a one-to-one mapping relationship between each pixel and semantic categories, and perform segmentation based on semantic information. It is widely used in scene understanding and automatic driving. , medical image analysis, robot vision and other fields. Image semantic segmentation is the cornerstone of image understanding, and the quality of its segmentation results will directly affect the processing of subsequent image content. Therefore, the research on image semantic segmentation technology has very important practical significance. [0003] Most of the traditional image semantic segmentation methods rely on manual ...

AI Technical Summary

Problems solved by technology

[0005] Disadvantages of the above semantic segmentation methods: first, the model input is generally an RGB image, and the input is too single, which may lead to the loss of local features; second, these methods are based on convolutional neural networks for feature extraction, and do not make full use of images. The local feature information and global context dependence of the image lead to very rough segmentation edges of the image and very low segmentation accuracy.

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