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Computer Science > Computer Vision and Pattern Recognition

arXiv:2309.00514 (cs)
[Submitted on 1 Sep 2023]

Title:A Machine Vision Method for Correction of Eccentric Error: Based on Adaptive Enhancement Algorithm

Authors:Fanyi Wang, Pin Cao, Yihui Zhang, Haotian Hu, Yongying Yang
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Abstract:In the procedure of surface defects detection for large-aperture aspherical optical elements, it is of vital significance to adjust the optical axis of the element to be coaxial with the mechanical spin axis accurately. Therefore, a machine vision method for eccentric error correction is proposed in this paper. Focusing on the severe defocus blur of reference crosshair image caused by the imaging characteristic of the aspherical optical element, which may lead to the failure of correction, an Adaptive Enhancement Algorithm (AEA) is proposed to strengthen the crosshair image. AEA is consisted of existed Guided Filter Dark Channel Dehazing Algorithm (GFA) and proposed lightweight Multi-scale Densely Connected Network (MDC-Net). The enhancement effect of GFA is excellent but time-consuming, and the enhancement effect of MDC-Net is slightly inferior but strongly real-time. As AEA will be executed dozens of times during each correction procedure, its real-time performance is very important. Therefore, by setting the empirical threshold of definition evaluation function SMD2, GFA and MDC-Net are respectively applied to highly and slightly blurred crosshair images so as to ensure the enhancement effect while saving as much time as possible. AEA has certain robustness in time-consuming performance, which takes an average time of 0.2721s and 0.0963s to execute GFA and MDC-Net separately on ten 200pixels 200pixels Region of Interest (ROI) images with different degrees of blur. And the eccentricity error can be reduced to within 10um by our method.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
Cite as: arXiv:2309.00514 [cs.CV]
  (or arXiv:2309.00514v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2309.00514

Submission history

From: Fanyi Wang [view email]
[v1] Fri, 1 Sep 2023 15:06:39 UTC (2,396 KB)
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