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Real-Time Product Label Classification in a Manufacturing Environment

Nederlund Persson, Oliver LU and Olsson, Anton LU (2024) DABN01 20241
Department of Statistics
Department of Economics
Abstract
In this thesis, a convolutional neural network was designed to perform real-time image classifications of ten product classes within a fast-paced, unstable manufacturing environment for quality control, while also linking it to a business case. Out of six eligible models that performed well on test data, only one proved good performance in simulated test runs, using out-of-sample test data. The find ings of this thesis highlights the necessity of maintaining a critical perspective on results obtained from test data during model evaluations within a manufacturing environment and that additional tests for validating initial results might be required. Furthermore, this thesis presents a framework for implementing computer vision in a... (More)
In this thesis, a convolutional neural network was designed to perform real-time image classifications of ten product classes within a fast-paced, unstable manufacturing environment for quality control, while also linking it to a business case. Out of six eligible models that performed well on test data, only one proved good performance in simulated test runs, using out-of-sample test data. The find ings of this thesis highlights the necessity of maintaining a critical perspective on results obtained from test data during model evaluations within a manufacturing environment and that additional tests for validating initial results might be required. Furthermore, this thesis presents a framework for implementing computer vision in a manufacturing environment. (Less)
Please use this url to cite or link to this publication:
author
Nederlund Persson, Oliver LU and Olsson, Anton LU
supervisor
organization
course
DABN01 20241
year
type
H1 - Master's Degree (One Year)
subject
keywords
Computer vision, manufacturing, convolutional neural networks
language
English
id
9163547
date added to LUP
2024-09-25 14:43:16
date last changed
2024-09-25 14:43:16
@misc{9163547,
  abstract     = {{In this thesis, a convolutional neural network was designed to perform real-time image classifications of ten product classes within a fast-paced, unstable manufacturing environment for quality control, while also linking it to a business case. Out of six eligible models that performed well on test data, only one proved good performance in simulated test runs, using out-of-sample test data. The find ings of this thesis highlights the necessity of maintaining a critical perspective on results obtained from test data during model evaluations within a manufacturing environment and that additional tests for validating initial results might be required. Furthermore, this thesis presents a framework for implementing computer vision in a manufacturing environment.}},
  author       = {{Nederlund Persson, Oliver and Olsson, Anton}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Real-Time Product Label Classification in a Manufacturing Environment}},
  year         = {{2024}},
}