Textile material CT image segmentation method and device based on convolutional neural network

Abstract

The invention discloses a textile material CT image segmentation method and device based on a convolutional neural network. The method comprises the steps: firstly building a segmentation model comprising an encoder and a decoder; secondly, acquiring a CT image of the textile material to be segmented; inputting the data into a trained segmentation model; performing feature extraction on the inputimage to obtain an encoding feature map via the encoder; inputting the encoding feature maps of the multiple levels into the corresponding levels of the decoder for feature fusion to obtain a first fusion feature map via the encoder; decoding the first fusion feature map to obtain a decoded feature map via the decoder; fusing the decoding feature maps of the middle level and the deep level to obtain a second fused feature map. The textile material CT image segmentation method solves the technical problems that in an existing textile material CT image segmentation method, manual segmentation isadopted, the segmentation difficulty is large, the segmentation process is tedious, time and labor are wasted, the segmentation result depends on experience and knowledge of operators to a large extent, and the segmentation result is difficult to reappear.

Description

technical field [0001] The present application relates to the technical field of image segmentation, in particular to a convolutional neural network-based CT image segmentation method and device for textile materials. Background technique [0002] Image processing is an important step in analyzing industrial CT images, which can make the images more intuitive and clear, and help improve detection efficiency and accuracy. Image segmentation is an important part of image processing. The segmentation of CT images mainly involves three related problems: changing noise, uncertainty of pixel gray level classification, and gray level imbalance. The CT images of textile materials generally have the characteristics of uneven gray scale and blurred edges, which bring great challenges to image segmentation. The existing CT image segmentation methods of textile materials mainly use manual segmentation. Although manual segmentation has high precision, it is difficult to segment, the seg...

AI Technical Summary

Problems solved by technology

[0003] This application provides a textile material CT image segmentation method and device based on a convolutional neural network, which is used to solve the problem that the existing textile material CT image segmentation method adopts manual segmentation. The segmentation is difficult, the segmentation process is cumbersome, time-consuming and laborious, Segmentation results largely depend on the experience and knowledge of the operator, technical problems that are difficult to reproduce the segmentation results

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