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DC Field | Value | Language |
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dc.contributor.author | Adibhatla, VA | - |
dc.contributor.author | Chih, H-C | - |
dc.contributor.author | Hsu, C-C | - |
dc.contributor.author | Cheng, J | - |
dc.contributor.author | Abbod, MF | - |
dc.contributor.author | Shieh, J-S | - |
dc.date.accessioned | 2021-06-21T12:20:43Z | - |
dc.date.available | 2021-06-21T12:20:43Z | - |
dc.date.issued | 2021-05-21 | - |
dc.identifier | ORCID iD: Maysam F. Abbod https://orcid.org/0000-0002-8515-7933 | - |
dc.identifier.citation | Adibhatla, V.A. et al. (2021) 'Applying deep learning to defect detection in printed circuit boards via a newest model of you-only-look-once', Mathematical Biosciences and Engineering, 18 (4): 4411-4428. doi: 10.3934/mbe.2021223. | en_US |
dc.identifier.issn | 1547-1063 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/22884 | - |
dc.description.abstract | Copyright © 2021 the Author(s), In this paper, a new model known as YOLO-v5 is initiated to detect defects in PCB. In the past many models and different approaches have been implemented in the quality inspection for detection of defect in PCBs. This algorithm is specifically selected due to its efficiency, accuracy and speed. It is well known that the traditional YOLO models (YOLO, YOLO-v2, YOLO-v3, YOLO-v4 and Tiny-YOLO-v2) are the state-of-the-art in artificial intelligence industry. In electronics industry, the PCB is the core and the most basic component of any electronic product. PCB is almost used in each and every electronic product that we use in our daily life not only for commercial purposes, but also used in sensitive applications such defense and space exploration. These PCB should be inspected and quality checked to detect any kind of defects during the manufacturing process. Most of the electronic industries are focused on the quality of their product, a small error during manufacture or quality inspection of the electronic products such as PCB leads to a catastrophic end. Therefore, there is a huge revolution going on in the manufacturing industry where the object detection method like YOLO-v5 is a game changer for many industries such as electronic industries. | en_US |
dc.description.sponsorship | Ministry of Science and Technology, Taiwan | en_US |
dc.format.extent | 4411 - 4428 | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en | en_US |
dc.publisher | American Institute of Mathematical Sciences (AIMS) | en_US |
dc.rights | Copyright © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0) | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | - |
dc.subject | convolution neural network | en_US |
dc.subject | YOLO-v5 | en_US |
dc.subject | deep learning | en_US |
dc.subject | printed circuit board (PCB) | en_US |
dc.title | Applying deep learning to defect detection in printed circuit boards via a newest model of you-only-look-once | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.3934/mbe.2021223 | - |
dc.relation.isPartOf | Mathematical Biosciences and Engineering | - |
pubs.issue | 4 | - |
pubs.publication-status | Published | - |
pubs.volume | 18 | - |
dc.identifier.eissn | 1551-0018 | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0) | 1.4 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License