ENHANCING QUALITY CONTROL: A DEEP LEARNING APPROACH FOR VISUAL INSPECTION

Registration ID: IJNRD_220011

Published ID: IJNRDTH00144

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S.MANOJ KUMAR, M.MARKANDAYAN, P. YUVARAJ, B.ASWINKANTH

Quality assurance , Deep learning , Defect detection , visual inspection method.

The production and distribution of bottled water have witnessed exponential growth globally, driven by factors such as convenience, health consciousness, and urbanization. With this surge in demand, ensuring the quality and integrity of bottled water products has become a top priority for manufacturers. Central to this endeavour is the need for effective inspection methods to detect and mitigate defects that may compromise product safety and consumer satisfaction. With the proliferation of bottled water consumption, ensuring the quality and safety of water bottles has become increasingly vital. Visual inspection methods provide a non-invasive and efficient means of identifying defects in water bottles during manufacturing processes. In this study, we propose a novel approach for the visual inspection of water bottles using YOLO, a deep learning architecture known for its effectiveness in image classification tasks. The proposed system employs YOLO algorithm to analyse images of water bottles captured by cameras installed along the production line. By leveraging the hierarchical feature representations learned by YOLO algorithm, our method aims to accurately classify water bottles into categories such as "defective" or "acceptable" based on the presence of defects such as scratches, dents, or impurities. We also explore strategies for optimizing model hyperparameters and training parameters to improve classification performance.

Published Paper   E-Certificate

S.MANOJ KUMAR, M.MARKANDAYAN, P. YUVARAJ, & B.ASWINKANTH (May-2024). ENHANCING QUALITY CONTROL: A DEEP LEARNING APPROACH FOR VISUAL INSPECTION. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), 340-402. https://ijnrd.org/papers/IJNRDTH00144.pdf

Volume 9 Issue 5, May-2024

Pages : 340-402

Paper Reg. ID: IJNRD_220011

Published Paper Id: IJNRDTH00144

Downloads: 000122031

Research Area: Computer Engineering 

Author Type: Indian Author

Country: madurai, tamil nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRDTH00144.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRDTH00144

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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