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Revision of an artificial neural network enabling industrial sorting
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Convolutional artificial neural networks can be applied for image-based object classification to inform automated actions, such as handling of objects on a production line. The present thesis describes theoretical background for creating a classifier and explores the effects of introducing a set of relatively recent techniques to an existing ensemble of classifiers in use for an industrial sorting system.The findings indicate that it's important to use spatial variety dropout regularization for high resolution image inputs, and use an optimizer configuration with good convergence properties. The findings also demonstrate examples of ensemble classifiers being effectively consolidated into unified models using the distillation technique. An analogue arrangement with optimization against multiple output targets, incorporating additional information, showed accuracy gains comparable to ensembling. For use of the classifier on test data with statistics different than those of the dataset, results indicate that augmentation of the input data during classifier creation helps performance, but would, in the current case, likely need to be guided by information about the distribution shift to have sufficiently positive impact to enable a practical application. I suggest, for future development, updated architectures, automated hyperparameter search and leveraging the bountiful unlabeled data potentially available from production lines.

Place, publisher, year, edition, pages
2019.
Series
TVE ; 19001
Keywords [en]
artificial neural networks, machine learning, deep learning, connectionism, pattern recognition, machine learning, automation, image analysis, information technology, applied mathematics, mathematical optimization, information theory, mathematical statistics, mathematical models, stochastic models, probabilities, chance, approximations, algorithms, computer programs, computer software, signal processing, high performance computing, numerical methods, high technology industries, sustainable development
Keywords [sv]
artificiella neurala nätverk, maskininlärning, djup maskininlärning, konnektionism, mönsterigenkänning, automatisering, bildanalys, informationsteknik, tillämpad matematik, optimering, informationsteori, statistisk inferens, matematiska modeller, stokastiska modeller, sannolikhetskalkyl, slumpen, approximationer, algoritmer, datorprogram, programvara, signalbehandling, högpresterande beräkningar, numeriska metoder, teknikutveckling, maskinindustri, högteknologisk industri, maskinhandel, skrothandel, bärkraftig utveckling
National Category
Robotics Computer Vision and Robotics (Autonomous Systems) Probability Theory and Statistics Other Computer and Information Science Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-392690OAI: oai:DiVA.org:uu-392690DiVA, id: diva2:1349274
External cooperation
Refind Technologies
Presentation
2019-04-02, 10234, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 13:15 (English)
Supervisors
Examiners
Available from: 2019-09-09 Created: 2019-09-07 Last updated: 2019-09-09Bibliographically approved

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RoboticsComputer Vision and Robotics (Autonomous Systems)Probability Theory and StatisticsOther Computer and Information ScienceSignal Processing

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CiteExportLink to record
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Citation style
  • apa
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Output format
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