Long working distance portable smartphone microscopy for metallic mesh defect detection

TL;DR

This paper introduces a novel long-working distance reflective smartphone microscopy system (LD-RSM) for non-destructive metallic mesh defect detection, combining optical innovation with advanced image processing for industrial inspection.

Contribution

The paper presents a new smartphone microscopy system with extended working distance and a dual prior weighted RPCA method for improved defect detection accuracy.

Findings

Achieves 4.92μm resolution and 22.23mm working distance.

Pixel-level defect detection accuracy of 84.8%.

Enhanced in-situ inspection capabilities for metallic mesh.

Abstract

Metallic mesh is a transparent electromagnetic shielding film with a fine metal line structure. However, it can develop defects that affect the optoelectronic performance whether in the production preparation or in actual use. The development of in-situ non-destructive testing (NDT) devices for metallic mesh requires long working distances, reflective optical path design, and miniaturization. To address the limitations of existing smartphone microscopes, which feature short working distances and inadequate transmission imaging for industrial in-situ inspection, we propose a novel long-working distance reflective smartphone microscopy system (LD-RSM). LD-RSM builds a 4f optical imaging system with external optical components and a smartphone, utilizing a beam splitter to achieve reflective imaging with the illumination system and imaging system on the same side of the sample. It achieves an optical resolution of 4.92$\mu$m and a working distance of up to 22.23 mm. Additionally, we introduce a dual prior weighted Robust Principal Component Analysis (DW-RPCA) for defect detection. This approach leverages spectral filter fusion and Hough transform to model different defect types, enhancing the accuracy and efficiency of defect identification. Coupled with an optimized threshold segmentation algorithm, DW-RPCA method achieves a pixel-level accuracy of 84.8%. Our work showcases strong potential for growth in the field of in-situ on-line inspection of industrial products.