An application of object detection in ship navigation

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dc.contributor.author Δημητρέλος, Ιωάννης el
dc.contributor.author Dimitrelos, Ioannis en
dc.date.accessioned 2022-11-10T11:13:47Z
dc.date.available 2022-11-10T11:13:47Z
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/56119
dc.identifier.uri http://dx.doi.org/10.26240/heal.ntua.23817
dc.rights Default License
dc.subject Safe ship navigation en
dc.subject Object Detection en
dc.subject Neural Networks en
dc.subject Autonomous ships en
dc.subject Yolo en
dc.subject Ασφαλής πλοήγηση πλοίων el
dc.subject Νευρωνικά Δίκτυα el
dc.subject Αυτόνομα πλοία el
dc.subject Αναγνώριση εμποδίων el
dc.title An application of object detection in ship navigation en
heal.type bachelorThesis
heal.classification Ship navigation en
heal.access free
heal.recordProvider ntua el
heal.publicationDate 2022-07-13
heal.abstract The present thesis is an attempt to light a spark in the field of object detection in ship navigation. This introduction is followed by an application based on an object detection model, named You Only Look Once (YOLO). Even though it is not the first time an object detection algorithm was applied to ship navigation problems, it is very fascinating to further research and experiment in this field by firstly testing an already existing algorithm and commenting on the results. Many object detection algorithms have been proposed with approaches ranging from traditional to deep learning. However, the majority of them have limited applications in real-time applications as they are computationally intensive and have accuracy problems. Another challenge when dealing with ship navigation is the wide range of background sizes of the objects. To overcome these problems the most recent object detection algorithm was selected. In this thesis, the You Only Look Once version 5 (YOLOv5) model was used, which is created in 2020 and is fine-tuned with the more recent and best practices in object detection and also is constantly modified and readjusted to achieve better results. From the different models of YOLOv5, the smaller one was picked, because of its size and the comparatively good accuracy it performs. After the model was chosen, a sufficient database for the model’s training was created using images that contained the most common obstacles that a ship can face. These are other ships, buoys, humans on the surface, containers, and rocks. The images were categorized into 3 teams, ship, floating object, and rock, which are the classes of the problem. The images were then labeled in a way that the YOLO accepts as input while some of them were kept and created the validation dataset. With these data some scenarios were created, namely, images that contained only one class in them, images of ship at night, and images of at least 2 classes coexisting in the same image and their combination. The model was trained with these different datasets and the results were collected, compared, and analyzed. The model achieved a Precision of 84%, Recall of 74%, and mAP of 79% on average when trained with 400 images of all the classes combined for 300 epochs and batch size 8. The model was also tested in a real-time application using a video and it detected all of the ships in most cases with 33 frames per second reload time. en
heal.advisorName Βεντίκος, Νικόλαος el
heal.committeeMemberName Ηλιοπούλου, Ελευθερία el
heal.committeeMemberName Γρηγορόπουλος, Γρηγόρης el
heal.academicPublisher Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ναυπηγών Μηχανολόγων Μηχανικών el
heal.academicPublisherID ntua
heal.numberOfPages 93 σ. el
heal.fullTextAvailability false

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