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Κούγκουλα, Μαγδαληνή

Master Thesis

Author

Κούγκουλα, Μαγδαληνή

Date

2022

Abstract

The aim of this dissertation is to use real-time video to locate and classify distinct motion objects. Two ways were employed and compared to achieve this. The Berkeley DeepDrive dataset was used to train the two YOLO and Faster R CNN models so that they could compare their performance and create a similar mAP table as well as matching diagrams of normalized total loss and average accuracy (mAP). Then, with a focus on autonomous driving and attempting to compare the models' performance, brief FPS and mAP measurement movies were generated.

Postgraduate Studies Programme

Τεχνητή Νοημοσύνη - Artificial Intelligence

Department

Σχολή Τεχνολογιών Πληροφορικής και Επικοινωνιών. Τμήμα Ψηφιακών Συστημάτων

Corporate Department

Εθνικό Κέντρο Έρευνας Φυσικών Επιστημών «Δημόκριτος», Ινστιτούτο Πληροφορικής και Τηλεπικοινωνιών

Number of pages

Language

Greek

URI

https://dione.lib.unipi.gr/xmlui/handle/unipi/14278
http://dx.doi.org/10.26267/unipi_dione/1701

Collections

Τμήμα Ψηφιακών Συστημάτων

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