Image processing and al applied to weldings

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dc.contributor.author Papachlimintzos, Ioannis en
dc.contributor.author Παπαχλιμίντζος Ιωάννης el
dc.date.accessioned 2023-02-01T11:55:41Z
dc.date.available 2023-02-01T11:55:41Z
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/57025
dc.identifier.uri http://dx.doi.org/10.26240/heal.ntua.24723
dc.rights Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/gr/ *
dc.subject Convolutional Neural Network en
dc.subject Unet en
dc.subject Weld seams en
dc.subject Semantic segmentation en
dc.subject Deep learning en
dc.subject Ραφές συγκόλλησης el
dc.subject Συνελικτικό νευρωνικό δίκτυο el
dc.subject Σημασιολογική τμηματοποίηση el
dc.subject Βαθιά μάθηση el
dc.title Image processing and al applied to weldings en
heal.type bachelorThesis
heal.classification Computer Science en
heal.classification Classification en
heal.classification Neural Networks en
heal.language en
heal.access free
heal.recordProvider ntua el
heal.publicationDate 2022-11-07
heal.abstract Marine automated inspections using Unmanned Areal Vehicles (UAVs), Remotely Operated Vehicles (ROVs) are emerging technologies which are constantly gaining ground. Intelligent vehicles is essential to have an environmental perception that provides crucial information about each ship’s feature so then to classify and inspect it according to the regulations. In this thesis, the main task is research about the application of image segmentation in welding joints. A Fully Convolution Neural Network is proposed based on UNet architecture which was modified so that VGG16 to be implemented as an encoder following a couple of transfer learning strategies. Decoder's convolutional layers were reduced by replacing one layer on each block with Batch Normalization and Dropout operations in order to minimize computational cost and increase model's accuracy. The dataset used for the training and testing of the model consists of images with welding joints which were collected from school's laboratory, ship surveys as well as from the internet (300 images) to achieve greater diversity and increase model’s robustness. The experimental results of the testing set show that the mean IoU is 0.46 and mean F1-score is 0.60. en
heal.advisorName Papalambrou, George en
heal.advisorName Samuelides, Manolis en
heal.committeeMemberName Zervaki, Anna en
heal.academicPublisher Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ναυπηγών Μηχανολόγων Μηχανικών. Τομέας Ναυτικής Μηχανολογίας. Εργαστήριο Ναυτικής Μηχανολογίας el
heal.academicPublisherID ntua
heal.numberOfPages 71 σ. el
heal.fullTextAvailability false

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Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα Except where otherwise noted, this item's license is described as Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα