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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