Image segmentation method based on ResNet and UNet models

An image segmentation and RGB image technology, applied in the field of image processing, can solve the problems of poor regional consistency, blurred boundaries, inaccurate feature extraction, etc., to achieve better effects, speed up training, and deepen the number of network layers.

Pending Publication Date: 2020-05-12
XIDIAN UNIV
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Problems solved by technology

[0007] The technical problem to be solved by the present invention is to provide an image segmentation method based on the ResNet and UNet models to improve the quality of image segmentation by utilizing the advantages of

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  • Image segmentation method based on ResNet and UNet models
  • Image segmentation method based on ResNet and UNet models
  • Image segmentation method based on ResNet and UNet models

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[0043] The present invention provides an image segmentation method based on ResNet and UNet models, by adjusting the size of the original RGB image and the corresponding label; inputting the RGB image to the UNet model for training; inputting the RGB image to the ResNet model, retaining the first three layers of output Replace the output of the third, fourth, and fifth layers of UNet; use the final training result as the segmentation model for image segmentation. The invention has the advantages of accurate feature extraction, good regional consistency of segmentation results, and retains the advantages of complete information, and can be used for image segmentation and target recognition.

[0044] ResNet is an image feature extraction network. Using the idea of ​​residuals, the network can maintain a continuous increase in accuracy when the depth increases. It is widely used in tasks such as classification; UNet network is an image segmentation network, which was originally appli...

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Abstract

The invention discloses an image segmentation method based on ResNet and UNet models, and the method comprises the steps of adjusting the size of an original RGB three-channel image, and correspondingly adjusting the size of a label image; taking the adjusted RGB image as the input of a UNet image segmentation module; taking the adjusted RGB image as the input of a ResNet feature extraction module, and reserving the output results of the first three layers to replace the output results of the third, fourth and fifth layers of UNet; obtaining an image segmentation training model based on ResNetand UNet, and training the model; and taking model parameters obtained by training as a prediction model, and carrying out image segmentation. According to the method, the quality of image segmentation is improved by utilizing the advantages of ResNet at the aspect of feature extraction, and the problems of inaccurate feature extraction, poor region consistency and fuzzy boundary which are easilygenerated when a single UNet model is applied to image segmentation are solved.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to an image segmentation method based on ResNet and UNet models. Background technique [0002] Image segmentation is the technology and process of dividing an image into several specific regions with unique properties and proposing objects of interest. Image segmentation is an important step from image processing to image analysis, and is the basis of object classification and recognition. There are four classic image segmentation methods: [0003] Threshold-based segmentation method: The principle of threshold segmentation method is to divide image pixels into several categories by setting different feature thresholds. Firstly, the feature value is found from the grayscale or color features in the original image according to certain criteria, and the image is divided into several parts. This type of method is simple to implement and has a small amount of calculation, bu...

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Application Information

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IPC IPC(8): G06T7/11G06N3/04G06N3/08
CPCG06T7/11G06N3/08G06T2207/10004G06N3/045Y02T10/40
Inventor 侯彪焦李成付勐马晶晶张向荣马文萍
Owner XIDIAN UNIV
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