Image segmentation method and system based on multi-branch feature fusion

Abstract

The invention discloses an image segmentation method and system based on multi-branch feature fusion, and the method comprises the steps of building three lightweight sub-networks based on Xception, carrying out the channel reduction operation and quadruple bilinear up-sampling of the output of the three sub-networks, obtaining the high-level feature output of the three sub-networks, then connecting the first advanced feature output and the second advanced feature output in parallel to a second feature extraction module and a third feature extraction module, and building an encoder of an image segmentation model; respectively carrying out channel reduction operation on the outputs of the three feature extraction sub-networks to obtain low-level features and high-level features, and constructing a decoder for completing an image segmentation model; constructing a loss function by using the two prediction images with different sizes; and performing optimization training on the loss function by using a stochastic gradient descent optimizer to obtain a trained image segmentation model, and completing an image segmentation task by using the trained image segmentation model. According to the invention, the segmentation accuracy is improved; the image segmentation speed is accelerated; and the detail part of the segmentation result image is improved.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and specifically relates to an image segmentation method and system based on multi-branch feature fusion, which can be used to distinguish the type of all pixels on a portable device recognition image, and can be used for geographic information measurement, medical image analysis and automatic driving And other issues. Background technique [0002] With the advancement of technology and the continuous updating of hardware equipment, the difficulty of obtaining images in daily life is getting lower and lower, and the processing requirements are getting bigger and bigger. Therefore, it is very important to understand and process images quickly. In the field of image processing, image segmentation is as important as image classification, so image segmentation has always been one of the focuses of scholars. [0003] Image segmentation is an important research topic in the field of computer v...

AI Technical Summary

Problems solved by technology

However, reducing the resolution of the input image will lead to a decrease in segmentation accuracy, because the information contained in the low-resolution image is much smaller than the information contained in the normal image.

Reducing some channels in the neural network will reduce the ability of the neural network to extract image features, resulting in the extracted features not being sufficient to support high-precision segmentation tasks, which will eventually lead to a decrease in segmentation accuracy.

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