Digital Image Correlation Framework for 3D Strain Measurement
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Solution Overview
Problem
Existing digital image correlation (DIC) methods are limited to 2D planar surfaces and small surfaces due to high pixel resolution requirements, making it difficult to perform accurate strain measurement on large 3D curved objects, as they struggle with image registration, camera lens distortion, and the need for precise multi-camera systems.
Innovation Solution
An end-to-end DIC framework incorporating image fusion and camera pose estimation using the perspective-n-point (PnP) method, specifically the RRWLM algorithm, to recover sharp images from blurry ones and project them onto a 3D surface, enabling the stitching and unfolding of images for accurate strain measurement on large 3D curved surfaces using a single camera.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single camera is used to capture images of large 3D curved surfaces, then the device complexity is reduced, but the measurement precision deteriorates due to image blur and distortion
Solution Approach 1:
The patent divides the large 3D curved surface into multiple overlapping image regions captured by a single moving camera. Each region is processed separately through deblurring and projection, then stitched together to form a complete full-field strain map, avoiding the need for a complex multi-camera system while maintaining measurement precision.
Solution Approach 2:
The patent transforms the 2D image data into 3D surface information by projecting deblurred images onto the 3D curved surface using camera pose estimation. This dimensional transformation enables accurate strain measurement on 3D surfaces using a single camera, resolving the contradiction between device simplicity and measurement accuracy.
2Measurement precision
If high pixel resolution is required for DIC analysis, then the measurement precision is improved, but the area of measurable surface deteriorates
Solution Approach 1:
The patent captures the large surface area through multiple overlapping images taken at different positions, then stitches these segmented images together. This segmentation approach allows high-resolution DIC analysis across the entire large surface area by processing each region separately and combining the results.
Solution Approach 2:
The patent creates multiple copies of the high-resolution image data through the moving camera capturing the surface from different positions. These image copies are then stitched together to form a complete high-resolution map of the large surface, enabling both high pixel resolution and large measurable area.
3Area of stationary object
If multiple cameras are used to cover large 3D surfaces, then the area of measurable surface is improved, but the device complexity and ease of operation deteriorate
Solution Approach 1:
The patent makes a single camera perform the function of multiple cameras by moving it to capture the entire large 3D surface. The single camera acquires multiple images from different positions, which are then stitched together to achieve the coverage area of a multi-camera system, greatly simplifying operation and calibration.
Solution Approach 2:
The patent uses the moving camera to automatically capture the entire surface area through its own movement and the image stitching process. The system self-adjusts to cover the large surface by taking multiple overlapping images and computationally combining them, eliminating the need for complex multi-camera setups and their associated calibration procedures.
Data Source
AI summary
An image processing method for measuring displacement of an object comprising is provided. The method includes acquiring first sequential images and second sequential images, wherein two adjacent images of the first sequential images include first overlap portions, wherein two adjacent images of the second sequential images include second overlap portions, wherein the first sequential images correspond to a first three dimensional (3D) surface on the object at a first state and the second sequential images correspond to a second 3D surface on the object at a second state. The method further includes deblurring the first sequential and second sequential images to obtain sharp focal plane images based on a blind deconvolution method, stitching the sharpened first sequential images and the sharpened second sequential images into a first sharp 3D image and a second sharp 3D image based on camera pose estimations by solving a perspective-n-point (PnP) problem using a refined robust weighted Levenberg Marquardt (RRWLM) algorithm, respectively. The method further comprises forming a first two-dimensional (2D) image and a second 2D image by unfolding, respectively, the first sharp 3D image and the second sharp 3D, and generating a displacement(strain) map image from the first 2D and second 2D images by performing a two-dimensional digital image correction (DIC) method.