Bibliographic Details
| Title: |
A Comparative Study of Structural Deformation Test Based on Edge Detection and Digital Image Correlation |
| Authors: |
Ruixiang Tang, Wenbing Chen, Yousong Wu, Hongbin Xiong, Banfu Yan |
| Source: |
Sensors, Vol 23, Iss 8, p 3834 (2023) |
| Publisher Information: |
MDPI AG, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Chemical technology |
| Subject Terms: |
deformation test, digital image processing, digital image correlation, Canny, Zernike moments, Chemical technology, TP1-1185 |
| More Details: |
Digital image-correlation (DIC) algorithms rely heavily on the accuracy of the initial values provided by whole-pixel search algorithms for structural displacement monitoring. When the measured displacement is too large or exceeds the search domain, the calculation time and memory consumption of the DIC algorithm will increase greatly, and even fail to obtain the correct result. The paper introduced two edge-detection algorithms, Canny and Zernike moments in digital image-processing (DIP) technology, to perform geometric fitting and sub-pixel positioning on the specific pattern target pasted on the measurement position, and to obtain the structural displacement according to the change of the target position before and after deformation. This paper compared the difference between edge detection and DIC in accuracy and calculation speed through numerical simulation, laboratory, and field tests. The study demonstrated that the structural displacement test based on edge detection is slightly inferior to the DIC algorithm in terms of accuracy and stability. As the search domain of the DIC algorithm becomes larger, its calculation speed decreases sharply, and is obviously slower than the Canny and Zernike moment algorithms. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1424-8220 |
| Relation: |
https://www.mdpi.com/1424-8220/23/8/3834; https://doaj.org/toc/1424-8220 |
| DOI: |
10.3390/s23083834 |
| Access URL: |
https://doaj.org/article/7e6e83d3c2e6413a872e56478f32657f |
| Accession Number: |
edsdoj.7e6e83d3c2e6413a872e56478f32657f |
| Database: |
Directory of Open Access Journals |
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