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M. Solomon, S. E. Eke, E. Ajulo, M. Aibinu, O. C. Ubadike


This work presents a Digital Image Processing (DIP) solution involving the use of image filters and processing functions to analyse the reference and the test images of a Printed Circuit Board (PCB) to yield a resulting image in which open and short circuits have been highlighted. The problem solved was the dependence of other PCB defects detection methods on the absence of deformation in the test image and this was done using the “Bounding Rectangle Approach”. The method involved the use of bounding rectangles to identify those PCB trace regions of interest and narrow down the focus for defect detection to the actual size of the PCB. The overall sensitivity of fault detection by this system was made variable and could be set by the system operator. However, the sensitivity setting used in this work was 0.75 (75%). This approach greatly helped reduce computational cost by allowing usage of a low specification computer (Raspberry Pi) to achieve automation of the PCB defect detection process.


Bounding Rectangle; Digital Image Processing; Exclusive OR; Image Subtraction; Printed Circuit Board; Thresholding

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N. K. Shinde and S. S. Morade, “PCB Inspection System Using Image Processing,” Int. J. Sci. Eng. Technol. Res., vol. 4, no. 4, pp. 1009–1012, 2015.

X. Tian, L. Zhao and H. Dong“Application of image processing in the detection of printed circuit board,” IEEE Workshop, pp. 157–159, 2014.

C. Szymanski and M. R. Stemmer, “Automated PCB inspection in small series production based on SIFT algorithm”, IEEE 24th Int. Symp., pp. 594–599, 2015.

K. P. Anoop, N. S. Sarath, and V. V. Sasi Kumar, “A Review of PCB Defect Detection Using Image Processing”, Certif. Int. J. Eng. Innov. Technol., vol. 4, no. 11, pp. 188-192, 2015.

M. Borthakur, A. Latne and P. Kulkarni, “A Comparative Study of Automated PCB Defect Detection Algorithms and to Propose an Optimal Approach to Improve the Technique”, Int. J. Comp. App., vol.114, no. 6, pp. 27-33, 2015.

A. Aravand and J. Sobhi, “The implementation of automated optical inspection in printed circuit boards”, Int. J. Comp. Sci. Net. Sec., vol. 17, no. 6, pp. 137–146, 2017.

A. Singh, V. H. Nayak and M. G. Vayada, “Automatic Detection of PCB Defects”, Int. J. Sci. Res. Dev., vol. 1, no. 6, pp. 285–289, 2014.

A. Suhasini, S. D. Kalro, B. G. Prathiksha, B. S. Meghashree, and H. D. Phaneendra, “PCB Defect Detection Using Image Subtraction Algorithm”, Int. J. Comp. Sci. Tre. Technol., vol. 3, no. 3, pp. 1–6, 2015.

B. Kaur, G. Kaur, and A. Kaur, “Detection and Classification of Printed Circuit Boards Defects”, Open Transac. Info. Pro., vol. 1, no. 1, pp. 8–16, 2014.

J. P. Nayak, B. D. Parameshachari, K. S. Soyjaudah, R. Banu and A. C Nuthan, “Identification of PCB Faults using Image Processing”, IEEE Int. Conf., pp. 1–4, 2017.

J. P. R. Nayak, K. Anitha, B. D. Parameshachari, R. Banu, and P. Rashmi, “PCB Fault Detection Using Image Processing”, IOP Conf. Ser. Mater. Sci. Eng., vol. 225, no. 1, p. 012244, 2017.

N. Dave, V. Tambade, B. Pandhare, and S. Saurav, “{PCB} Defect Detection Using Image Processing And Embedded System”, Int. Res. J. Eng. Technol., vol. 3, no. 5, pp. 1–5, 2016.

P. R. Masalkar and P. S. Kasliwal, “Study of the Image Processing algorithms for defect detection of PCBs .”, Int. J. Eng. Technol. Sci. Res., vol. 4, no. 6, pp. 606–612, 2017.

V. Chaudhary, I. R. Dave, and K. P. Upla, “Automatic Visual Inspection of Printed Circuit Board for Defect Detection and Classification”, IEEE Int. Conf., pp. 732–737, 2017.


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