Super-sensitivity optical flow structural deformation measurement
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Solution Overview
Problem
Existing displacement measurement methods for structural deformation, such as contact methods and non-contact methods like GPS and laser ranging, face limitations including low accuracy, stability issues, and the need for complex equipment and target arrangements. Vision-based image measurement methods like DIC are limited by the number of spatial measurement points and computational efficiency.
Innovation Solution
A non-contact dynamic measurement method based on a super-sensitivity optical flow method, which involves acquiring images of a structure, calibrating a scale parameter, extracting effective pixels, constructing a spatiotemporal matrix, performing singular value decomposition, and using weighted average filtering and optical flow calculations to determine physical displacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital image correlation (DIC) is used for displacement measurement, then measurement coverage can be achieved, but the number of spatial measurement points is severely limited by the number of targets and computational efficiency is low
Solution Approach 1:
The patent extracts only the effective pixels with significant spatial gradient values from the full image, constructing an effective pixel grayscale spatiotemporal matrix. This extraction approach reduces the data volume requiring correlation calculations while preserving the essential deformation information, thereby improving computational efficiency without sacrificing measurement precision
Solution Approach 2:
The patent introduces a grayscale gradient threshold parameter to filter pixels, transforming the measurement approach from processing all pixels to processing only effective pixels. This parameter change optimizes the balance between measurement coverage and computational efficiency by eliminating redundant calculation on pixels that do not contribute significantly to deformation measurement
2Measurement precision
If contact displacement measurement devices are used, then displacement can be measured, but fixing brackets are required which brings troubles in practical applications
Solution Approach 1:
The patent replaces the mechanical contact measurement system (displacement meters requiring fixing brackets) with a vision-based optical measurement system. By using image processing and optical flow methods, the patent achieves displacement measurement without any physical contact or mechanical attachment to the structure, significantly improving ease of operation and practical applicability
3Area of stationary object
If GPS measurement is used for structural deformation, then large area coverage is possible, but deflection accuracy is low and measurement stability is poor due to external factors
Solution Approach 1:
The patent introduces an image processing intermediary system between the camera and the structure being measured. By capturing images and processing them through effective pixel extraction and optical flow algorithms, the system achieves high-precision displacement measurement without direct physical contact, eliminating the need for GPS satellites and corner reflectors while improving both accuracy and stability
Data Source
AI summary
Disclosed are a non-contact dynamic measurement method, system, processing equipment and storage medium for structural deformation based on a super-sensitivity optical flow method. The method includes the following steps: acquiring images of a structure to be measured; calibrating a scale parameter; constructing an original grayscale spatiotemporal matrix of effective pixels by using the pixels with large image grayscale gradients; calculating and identifying structural deformation modes through singular value decomposition, and then constructing a weight matrix based on deformation shapes; performing weighted average filtering on the original grayscale spatiotemporal matrix by using the weight matrix, and restoring decimal parts of grayscale values lost due to digitization by using noise signals during imaging and signal dithering principles; and finally, calculating a pixel displacement time history of each effective pixel point through a gradient-based optical flow method, and converting through the scale parameter to obtain a physical displacement time history.