Long working distance portable smartphone microscopy for metallic mesh defect detection

TL;DR

To enable non-destructive, in-situ testing of metallic-mesh EMI shields, the authors design LD-RSM, a long-working-distance reflective smartphone microscope built around a 4f optical path and shared illumination/imaging on one side of the sample. They couple hardware innovations with a dual-prior RPCA algorithm (DW-RPCA) that fuses spectral-filter priors and Hough-transform priors to robustly separate background from defect signals in both square and circular meshes. Experimental results show WD of $22.23\,\mathrm{mm}$, resolution $4.92\,\mu\mathrm{m}$, and a field-of-view of $800\,\mu\mathrm{m}$, with DW-RPCA achieving the best defect-detection performance (f-values around $0.856$ for square and $0.848$ for circular meshes) compared with baselines. The system, including a UR5 robot for scanning and AirDroid transmission, demonstrates a low-cost, portable approach for industrial in-situ inspection of large-area micro-/nano-structured metallic meshes.

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$μ$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.

Figures (10)

• Figure 1: Schematic of the rapid prototyping of the LD-RSM system.
• Figure 2: Design and fabrication of the LD-RSM system. (a) Schematic of a 4f optical configuration. (b) 3D rendering of the LD-RSM system and (c) Prototype of the developed microscopic system.
• Figure 3: Image acquisition and transmission strategies. (a) Fixed mechanics for UR5 arm and LD-RSM, (b) UR5 robotic arm scanning programme, (c) LD-RSM Script for automatic image acquisition and transmission.
• Figure 4: The overall detection process of the DW-RPCA.
• Figure 5: Characterization of the LD-RSM system. (a) Image of a USAF-1951 resolution test target acquired by the LD-RSM. (b) Imaging result before improvement. (c) Imaging result after the measures to suppress stray light.