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Abstract: metallic mesh is a transparent electromagnetic shielding film with a fine metal line structure. However, in production preparation or actual use it can develop defects that affect the optoelectronic performance. 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 (LD-RSM) system. LD-RSM comprises a 4f optical imaging system with external optical components and a smartphone. This system uses 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 and a working distance of up to 22.23 mm. Additionally, we introduce dual-prior weighted robust principal component analysis (DW-RPCA) for defect detection. This approach leverages spectral filter fusion and the Hough transform to model different defect types, which enhances the accuracy and efficiency of defect identification. Coupled with a double-threshold segmentation approach, the DW-RPCA method achieves a pixel-level defect detection accuracy (f-value) of 0.856 and 0.848 in square and circular metallic mesh datasets, respectively. Our work shows strong potential in the field of in situ industrial product inspection.

基于长工作距便携式智能手机显微镜的金属网栅缺陷检测技术

[1]Ahmed I, Elsherif M, Ali M, et al., 2023. Photonic hydrogel for continuous glucose monitoring using smartphone readout. Mater Des, 231: 112065.

[2]Alper Selver M, Avşar V, Özdemir H, 2014. Textural fabric defect detection using statistical texture transformations and gradient search. J Text Inst, 105(9):998-1007.

[3]Bao XY, Liang JZ, Xia YF, et al., 2022. Low-rank decomposition fabric defect detection based on prior and total variation regularization. Visual Comput, 38(8):2707-2721.

[4]Baracu AM, Avram MA, Breazu C, et al., 2021. Silicon metalens fabrication from electron beam to UV-nanoimprint lithography. Nanomaterials, 11(9):2329.

[5]Bui TH, Thangavel B, Sharipov M, et al., 2023. Smartphone-based portable bio-chemical sensors: exploring recent advancements. Chemosensors, 11(9):468.

[6]Candés EJ, Li XD, Ma Y, et al., 2011. Robust principal component analysis. J ACM, 58(3):1-37.

[7]Cao JJ, Wang NN, Zhang J, et al., 2016. Detection of varied defects in diverse fabric images via modified RPCA with noise term and defect prior. Int J Cloth Sci Technol, 28(4):516-529.

[8]Cao JJ, Zhang J, Wen ZJ, et al., 2017. Fabric defect inspection using prior knowledge guided least squares regression. Multim Tools Appl, 76(3):4141-4157.

[9]Huang BX, Kang L, Tsang VTC, et al., 2024. Deep learning-assisted smartphone-based quantitative microscopy for label-free peripheral blood smear analysis. Biomed Opt Expr, 15(4):2636-2651.

[10]Huang TY, Wang HH, Peng CL, et al., 2015. A new remote desktop approach with mobile devices: design and implementation. Lect Notes Electr Eng, 331:305-321.

[11]Imamoglu N, Lin WS, Fang YM, 2013. A saliency detection model using low-level features based on wavelet transform. IEEE Trans Multim, 15(1):96-105.

[12]Janev A, Kang JS, Park SY, 2023. A smartphone integrated paper (SIP)-based platform for rapid and on-site screening of urinary tract infections. Sens Actuat B Chem, 382: 133498.

[13]Ji X, Liang JZ, Di L, et al., 2020. Fabric defect fetection via weighted low-rank decomposition and Laplacian regularization. J Eng Fibers Fabr, 15:1-14.

[14]Jing JF, Wang Z, Rätsch M, et al., 2022. Mobile-Unet: an efficient convolutional neural network for fabric defect detection. Text Res J, 92(1-2):30-42.

[15]Kheireddine S, Perumal AS, Smith ZJ, et al., 2019. Dual-phone illumination-imaging system for high resolution and large field of view multi-modal microscopy. Lab Chip, 19(5):825-836.

[16]Kim K, Lee WG, 2023. Portable, automated and deep-learning-enabled microscopy for smartphone-tethered optical platform towards remote homecare diagnostics: a review. Small Methods, 7(1): 2200979.

[17]Lai WQ, Chang YF, Chou FN, et al., 2022. Portable FRET-based biosensor device for on-site lead detection. Biosensors, 12(3):157.

[18]Lee KC, Lee K, Jung J, et al., 2021. A smartphone-based Fourier ptychographic microscope using the display screen for illumination. ACS Photon, 8(5):1307-1315.

[19]Li CL, Liu CD, Gao GS, et al., 2019. Robust low-rank decomposition of multi-channel feature matrices for fabric defect detection. Multim Tools Appl, 78(6):7321-7339.

[20]Li MX, Zarei M, Mohammadi K, et al., 2023. Silver meshes for record-performance transparent electromagnetic interference shielding. ACS Appl Mater Interf, 15(25):30591-30599.

[21]Li W, Wang HY, Huo LZ, et al., 2024. Low-rank matrix recovery with total generalized variation for defending adversarial examples. Front Inform Technol Electron Eng, 25(3):432-445.

[22]Li ZH, Li HK, Zhu XY, et al., 2022. Directly printed embedded metal mesh for flexible transparent electrode via liquid substrate electric-field-driven jet. Adv Sci, 9(14): 2105331.

[23]Liang YL, Huang XJ, Wen K, et al., 2023. Metal mesh-based infrared transparent EMI shielding window with balanced shielding properties over a wide frequency spectrum. Appl Sci, 13(8):4846.

[24]Liu GH, Li F, 2022. Fabric defect detection based on low-rank decomposition with structural constraints. Visual Comput, 38(2):639-653.

[25]Lu ZG, Zhang YL, Wang HY, et al., 2023. Transparent thermally tunable microwave absorber prototype based on patterned VO₂ film. Engineering, 29:198-206.

[26]Lu ZG, Cao ZB, Tan JB, 2024. A valid evaluation method for electromagnetic shielding efficiency of metallic mesh based on free-space reflection measurement. IEEE Trans Instrum Meas, 73: 6002207.

[27]Ng MK, Ngan HYT, Yuan XM, et al., 2014. Patterned fabric inspection and visualization by the method of image decomposition. IEEE Trans Autom Sci Eng, 11(3):943-947.

[28]Onwubolu GC, 2017. Introduction to SolidWorks: a Comprehensive Guide with Applications in 3D Printing. CRC Press, Boca Raton, USA.

[29]Qiu JF, Wu QH, Ding GR, et al., 2016. A survey of machine learning for big data processing. EURASIP J Adv Signal Process, 2016(1):67.

[30]Qiu TF, Akinoglu EM, Luo B, et al., 2022. Nanosphere lithography: a versatile approach to develop transparent conductive films for optoelectronic applications. Adv Mater, 34(19): 2103842.

[31]Rabha D, Biswas S, Hatiboruah D, et al., 2022a. An affordable, handheld multimodal microscopic system with onboard cell morphology and counting features on a mobile device. Analyst, 147(12):2859-2869.

[32]Rabha D, Rather MA, Mandal M, et al., 2022b. Programmable illumination smartphone microscopy (PISM): a multimodal imaging platform for biomedical applications. Opt Laser Eng, 151: 106931.

[33]Russell BC, Torralba A, Murphy KP, et al., 2008. LabelMe: a database and web-based tool for image annotation. Int J Comput Vis, 77(1-3):157-173.

[34]Sakhare K, Kulkarni A, Kumbhakarn M, et al., 2015. Spectral and spatial domain approach for fabric defect detection and classification. Int Conf on Industrial Instrumentation and Control, p.640-644.

[35]Wang BF, Li YW, Zhou MF, et al., 2023. Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence. Nat Commun, 14(1):1341.

[36]Wang HY, Ji CG, Zhang C, et al., 2019. Highly transparent and broadband electromagnetic interference shielding based on ultrathin doped Ag and conducting oxides hybrid film structures. ACS Appl Mater Interf, 11(12):11782-11791.

[37]Zhang YL, Cao JX, Lu ZG, et al., 2021. Comprehensive evaluation factor of optoelectronic properties for transparent conductive metallic mesh films. Front Inform Technol Electron Eng, 22(11):1532-1540.

[38]Zheng YH, Yin H, Zhou CX, et al., 2023. A hand-held platform for boar sperm viability diagnosis based on smartphone. Biosensors, 13(11):978.

[39]Zhou CF, Liu MQ, Zhang SL, et al., 2023. A graph-based two-stage classification network for mobile screen defect inspection. Front Inform Technol Electron Eng, 24(2):203-216.

[40]Zhou J, Wang J, Bu HG, 2017. Fabric defect detection using a hybrid and complementary fractal feature vector and FCM-based novelty detector. Fibres Text East Eur, 25(6):46-52.

[41]Zhu WB, Pirovano G, O’Neal PK, et al., 2020. Smartphone epifluorescence microscopy for cellular imaging of fresh tissue in low-resource settings. Biomed Opt Expr, 11(1):89-98.

[42]Zhu XY, Liu MY, Qi XM, et al., 2021. Templateless, plating-free fabrication of flexible transparent electrodes with embedded silver mesh by electric-field-driven microscale 3D printing and hybrid hot embossing. Adv Mater, 33(21): 2007772.