Quality Control Using Machine Vision
Bhattarai, Ashish (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202503244825
https://urn.fi/URN:NBN:fi:amk-202503244825
Tiivistelmä
The bottled water industry is growing rapidly, encountering quality control hurdles as a result
of its expansion pace. Conventional quality control approaches are struggling to keep up with
the demands for fast production speeds which could result in inefficiencies and even product
recalls. This study explores the application of machine vision and deep learning to streamline
identification in the water manufacturing process.
In particular it utilizes the YOLO version 8 framework to spot problems, like missing labels,
wrong bottle sizes and irregular water levels. By introducing a quality assurance system in
place, the research seeks to enhance the precision, efficiency and dependability of identifying
defects when compared to conventional automated techniques. The study highlights the role
of machine vision technologies in revolutionizing quality assurance procedures leading to
product safety and cost savings.
of its expansion pace. Conventional quality control approaches are struggling to keep up with
the demands for fast production speeds which could result in inefficiencies and even product
recalls. This study explores the application of machine vision and deep learning to streamline
identification in the water manufacturing process.
In particular it utilizes the YOLO version 8 framework to spot problems, like missing labels,
wrong bottle sizes and irregular water levels. By introducing a quality assurance system in
place, the research seeks to enhance the precision, efficiency and dependability of identifying
defects when compared to conventional automated techniques. The study highlights the role
of machine vision technologies in revolutionizing quality assurance procedures leading to
product safety and cost savings.