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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM17016

MACHINE VISION AND IMAGE PROCESSING BASED DEFECT DETECTING USING RASPBERRY PI

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM17016

Volume:

6 |

11 |

Nov 2022

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

PPST

Prof. Pradeep. S. Togarikar

VTP

Vishal T Pardeshi

VBK

Vaibhav B Kavade

SBG

Swapnil B Gajare

HSK

Harshada S Kawade

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Abstract

– The manufacture of printed circuit boards involves various steps such as structuring, etching, drilling , masking and printing labels. Shorts between traces and pads, open circuits, and various defects can occur during the manufacture of PCB hardware. This white paper resents his unique technique for identifying defects in PCBs using image processing and image processing techniques. The system reduces PCB inspection time and improves accuracy even when the image scale is different with respect to the reference image. The proposed automated PCB inspection system significantly reduces the time taken to detect defects. Accurate results speed up the inspection process and help improve productivity. Easily identify opens and shorts, as well as detect wire gaps, voids, flaws and defects. This automatic inspection system can be introduced at low cost. Smaller industries can adopt this system to reduce inspection time and minimize errors.

KEYWORDS

Circuit Boards Fault Detection Vision Raspberry Pi Machine Vision Sensors Cameras etc.

Cite This Article

Prof. Pradeep. S. Togarikar, Vishal T Pardeshi, Vaibhav B Kavade, Swapnil B Gajare, Harshada S Kawade, "MACHINE VISION AND IMAGE PROCESSING BASED DEFECT DETECTING USING RASPBERRY PI," International Journal of Scientific Research in Engineering and Management, vol. 6, no. 11, Nov 2022.

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Publication Date

11/29/2022

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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0

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1

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5

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The authors declare no conflict of interest related to this research work.

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No funding information available for this research.

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