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• W2789841922 abstract "Inspection of printed circuit board (PCB) has been a crucial process in the electronic manufacturing industry to guarantee product quality & reliability, cut manufacturing cost and to increase production. The PCB inspection involves detection of defects in the PCB and classification of those defects in order to identify the roots of defects. In this paper, all 14 types of defects are detected and are classified in all possible classes using referential inspection approach. The proposed algorithm is mainly divided into five stages: Image registration, Pre-processing, Image segmentation, Defect detection and Defect classification. The algorithm is able to perform inspection even when captured test image is rotated, scaled and translated with respect to template image which makes the algorithm rotation, scale and translation in-variant. The novelty of the algorithm lies in its robustness to analyze a defect in its different possible appearance and severity. In addition to this, algorithm takes only 2.528 s to inspect a PCB image. The efficacy of the proposed algorithm is verified by conducting experiments on the different PCB images and it shows that the proposed afgorithm is suitable for automatic visual inspection of PCBs." @default.
• W2789841922 created "2018-03-29" @default.
• W2789841922 creator A5002879297 @default.
• W2789841922 creator A5036829151 @default.
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• W2789841922 date "2017-03-01" @default.
• W2789841922 modified "2023-10-16" @default.
• W2789841922 title "Automatic visual inspection of printed circuit board for defect detection and classification" @default.
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• W2789841922 doi "https://doi.org/10.1109/wispnet.2017.8299858" @default.
• W2789841922 hasPublicationYear "2017" @default.
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