Phase information extraction method based on convolutional neural network, storage medium and equipment

A technology of convolutional neural network and phase information, which is applied in the field of phase information extraction based on Hypercolumns convolutional neural network, can solve the problem that closed fringe images cannot accurately extract phase information, and achieve the effect of high phase extraction accuracy and fast processing speed

Active Publication Date: 2020-12-22
XI AN JIAOTONG UNIV
View PDF9 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the application of this method has certain limitations, and the phase information cannot be accurately extracted for images with closed fringes

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Phase information extraction method based on convolutional neural network, storage medium and equipment
  • Phase information extraction method based on convolutional neural network, storage medium and equipment
  • Phase information extraction method based on convolutional neural network, storage medium and equipment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0045] The invention provides a phase information extraction method, storage medium and equipment based on a convolutional neural network. The phase data extraction problem in three-dimensional contour measurement is regarded as a regression task, and the hypercolumns convolutional neural network constructed based on deep learning technology The network implements this function. First, the Hypercolumns convolutional neural network model is constructed, and the definitions and functional modules of its layers are introduced in detail; then four different mathematical functions are used to generate training data sets to train the neural network model, and the training strategy of the method of the present invention is determined at the same time and verification method; finally, aiming at the data flaws in the initial results predicted by the network model, the polynomial three-dimensional surface fitting technology is used to eliminate local errors and realize the optimization o...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a phase information extraction method based on a convolutional neural network, a storage medium and equipment. The method comprises the steps: constructing a Hypercolums convolutional neural network model, carrying out the analysis and prediction of an interference fringe image, and obtaining the phase data corresponding to the interference fringe image; four different mathematical functions of a sine / cosine data set, adopting a quadric surface data set, a waveform data set and a free-form surface data set respectively for generating phase data in a sample set, and thenobtaining an interference image corresponding to the phase data through an interference fringe image light intensity distribution formula; equally dividing the generated samples to jointly form N groups of data in the training set and M groups of data in the verification set; then, based on all the generated sample data, training a Hypercolums convolutional neural network model; and adopting a polynomial three-dimensional surface fitting method to eliminate local errors of the initial prediction result of the Hypercolums convolutional neural network, and realizing the phase extraction resultoptimization. The method is high in processing speed and high in phase extraction precision, and a single-frame interferogram phase extraction function can be realized.

Description

technical field [0001] The invention belongs to the technical field of interference fringe image processing in the field of three-dimensional contour precision measurement, and in particular relates to a phase information extraction method, storage medium and equipment based on a Hypercolumns convolutional neural network. Background technique [0002] Optical three-dimensional profilometry technology is widely used in industrial manufacturing, reverse engineering, aerospace, medical diagnosis and other fields. It has the characteristics of non-contact, high precision and high resolution, and is recognized as one of the most promising profilometry methods. There are many methods to realize optical profilometry, including time method, structured light method, projection method, interferometry and so on. In the interferometric measurement process, the test light is reflected by the workpiece and superimposed and interfered with the reference light to form an interference fringe...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/13G06T5/30G06N3/04G06N3/08
CPCG06T7/13G06T5/30G06N3/08G06T2207/20081G06T2207/20084G06T2207/30164G06N3/045
Inventor 李兵赵卓路嘉晟康晓清刘桐坤
Owner XI AN JIAOTONG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products