Detection and classification of marine vehicles
Examensarbete för masterexamen
https://hdl.handle.net/20.500.12380/304400
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2021-82 Athanasios Rofalis.pdf | Master Thesis | 1.67 MB | Adobe PDF | View/Open |
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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 |
URI: | https://hdl.handle.net/20.500.12380/304400 |
Collection: | Examensarbeten för masterexamen // Master Theses |
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