Image semantic segmentation method and device based on codec

Abstract

The embodiment of the invention provides an image semantic segmentation method and device based on a codec. The method comprises the following steps: inputting a to-be-detected image into an encoder of a preset image semantic segmentation network model, extracting features by using a convolutional network, respectively inputting the features into a plurality of pooling layers with different sizes,and performing feature fusion according to output results of the plurality of pooling layers with different sizes to obtain a high-level semantic feature map of the to-be-detected image; inputting the feature map into a decoder of an image semantic segmentation network model to obtain a detection result of semantic analysis, wherein the image semantic segmentation network model is obtained by training a sample image with a determined semantic label. Due to the fact that local and global information is fused in the pooling layers of different sizes, learning of targets of different sizes is facilitated through the multi-scale feeling domain, and therefore an accurate high-level semantic feature map of the image to be detected can be obtained. After the decoder is used for analysis, the segmentation precision of the target boundary in the detection result of semantic analysis is higher.

Description

technical field [0001] The present invention relates to the field of semantic segmentation based on deep learning, in particular to a codec-based image semantic segmentation method and device. Background technique [0002] Image semantic segmentation is a key technology in the field of computer vision, which plays a vital role in tasks such as image understanding, scene analysis, and object tracking. Semantic segmentation is pixel-level image understanding, that is, labeling the category of each pixel in the image. Its task is to segment the image into several meaningful objects and assign a specific type of label to each object. The traditional image segmentation method divides the image into different regions according to the characteristics of image color, texture information and spatial structure. The same region has consistent semantic information, and the attributes of different regions are different. From the simplest threshold segmentation, region growing, edge dete...

AI Technical Summary

Problems solved by technology

Although the accuracy of the prediction results has been improved to a certain extent, the segmentation accuracy of the target boundary still needs to be improved.

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