Stereo visual calibration method integrating neural network and virtual target

Abstract

The invention discloses a stereo vision calibration method combining a neural network and a virtual target, comprising the following steps: S1, using a single corner point target to construct a stereo virtual target, acquiring a corner point image and recording the world three-dimensional coordinates of the corner point during the construction process ; S2, extract the pixel coordinates of the corner points in the image; S3, use the neural network to train the pixel coordinates of the corner points and the world three-dimensional coordinates; S4, input the test samples into the training neural network for three-dimensional reconstruction, and calculate the reconstruction error; S5, Change the number of hidden layer nodes of the neural network to minimize the error. On the one hand, the method of the present invention utilizes a single-corner checkerboard to construct a three-dimensional virtual target with a controllable range, which solves the problem of difficult production and processing of large targets; on the other hand, it uses a neural network to calibrate the camera without establishing complex nonlinear distortion model, the calibration accuracy is significantly higher than the linear calibration method. The invention is practical, simple and easy to operate and has high precision.

Description

technical field [0001] The invention belongs to the field of machine vision, and in particular relates to a binocular positioning method in large-field photogrammetry. Background technique [0002] Photogrammetry technology is widely favored due to its excellent characteristics such as non-contact, high precision and unlimited measurement points. Especially in recent years, the development of camera imaging materials and processing technology has provided the possibility for the wide application of this technology in large-scale industrial structures. [0003] Photogrammetry technology mainly faces the problem of measurement accuracy. The main difficulties of large field of view dual-target calibration are: 1. Due to the existence of nonlinear distortion of the camera, traditional calibration often needs to establish a complex camera model, which increases the calibration to a certain extent. Complicated and difficult; 2. Large-scale high-precision calibration requires corr...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to avoid the deficiencies in the prior art and provide a stereo vision calibration method combining neural network and virtual target to solve the problems of difficult production and processing of large targets, nonlinear distortion of cameras and complex calibration models

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