Structural Deformation Test Based on Edge Detection and Digital Image Correlation
Digital Image Processing(DIP)is widely utilized in mechanical manufacturing and automation engineering for precise large-scale dimensional measurement.DIP's non-contact,high-speed,high-precision,and cost-effective features make it a preferred choice.Concurrently,Digital Image Correlation(DIC)excels in real-time,sub-pixel-level deformation analysis in engineering structures.This study validates the feasibility of using DIP algorithms for quantifying structural displacements.We introduce two edge detection algorithms,Canny and Zernike moments,in this study.These algorithms identify edges onpatterned targets at measurement locations.Geometric fitting and sub-pixel localization are then applied to the results.Variations in target positions over time are scrutinized to assess structural displacement.We comprehensively compare the effectiveness of DIP-based and DIC-based deformation testing algorithms,considering precision,processing speed,and error rates.Our research reveals that DIP-based methods for assessing structural deformations exhibit a high level of precision,achieving an accuracy of 0.1 pixel in simulations and 0.1 millimeters in controlled laboratory conditions.Canny and Zernike moment algorithms offer comparable accuracy but fall short of DIC's precision.DIC's computational efficiency decreases with larger subsets,while Canny and DIC algorithms outperform Zernike moments in processing speed.By utilizing CUDA parallel processing,Zernike moment algorithms significantly enhance their speed,making them competitive with DIC and Canny algorithms,particularly in scenarios with smaller subsets.In practical bridge experiments,DIC achieves testing errors of less than 0.05 millimeters with the highest precision,while Canny and Zernike moment algorithms provide sub-millimeter precision,suitable for specialized engineering applications requiring structural displacement assessments.
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