Detection and classification of marine vehicles

Examensarbete för masterexamen

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Bibliographical item details
Type: Examensarbete för masterexamen
Title: Detection and classification of marine vehicles
Authors: Rofalis, Athanasios
Abstract: One of the most common tasks within the computer vision field is the detection and classification of different objects. This thesis aims to deliver a software that can be deployed into real world scenarios and mange to detect and classify marine vehicles accurately. Using one of the pre-defined deep neural network models You look only once (YOLO), we managed to achieve a high performance for the detection and classification task. The training of the model took place using a specific dataset of grayscale images, which led to a model that can classify the objects with an accuracy of 68% and predict the relevant position with mean average precision (mAP) of 0.77. Moreover, the model tested into different weather conditions and achieved an accyracy of 0.85% and mAP of 0.068. In general, the YOLO model seems to be a robust detector that can be trained and deployed for detecting efficiently objects with high performance. Keywords:
Keywords: Classification;detection;deep learning;computer vision;YOLO
Issue Date: 2021
Publisher: Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper
Series/Report no.: 2021:82
Collection:Examensarbeten för masterexamen // Master Theses

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