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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | EXPOSURE OF DEFECTED PCB USING PYTHON |
| Authors Name: | Ammati Santhi priya , Dr.S.Aruna , S.Keerthi Mahalakshmi , K.Supraja , M.Rama satya sai and B. sai siva sri |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_192656
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| Published Paper Id: | IJNRD2304616 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | http://doi.one/10.1729/Journal.33905 |
| Abstract: | Abstract : The importance of having a defect-free Printed Circuit Board (PCB) has increased in today's modern era due to the high demand for low-cost electronic items. Such systems require PCBs with no errors, making it crucial to detect defects and imperfections to create high-quality, error-free PCBs. To accomplish this, a defect detection system that uses images of the PCBs has been developed. The YOLO-v5 model is a new model that has been implemented to detect defects in PCBs. Many models and different approaches have been implemented in the past for the quality inspection of PCBs to detect defects. The YOLO models, including YOLO, YOLO-v2, YOLO-v3, YOLO-v4, and Tiny-YOLO-v2, are the state-of-the-art in the artificial intelligence industry. YOLO-v5 is a game changer for many industries such as the electronic industry as it provides excellent efficacy, precision, and speed. In this project, 1386 images with 6 different types of defects were used, and the YOLO-v5 algorithm was trained with a batch size of 16 and a trained epoch of 500. As a result, this model achieved a defect detection accuracy of over 90% mAP in PCBs |
| Keywords: | - Convolution Neural Network, YOLO-v5, Deep Learning, Printed Circuit Board (PCB) |
| Cite Article: | "EXPOSURE OF DEFECTED PCB USING PYTHON", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.g109-g115, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304616.pdf |
| Downloads: | 000118759 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2304616 Registration ID: 192656 Published In: Volume 8 Issue 4, April-2023 DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.33905 Page No: g109-g115 Country: VIJAYAWADA, Andhra Pradesh, India Research Area: Electronics & Communication Engg. Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2304616 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2304616 |
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ISSN: 2456-4184
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