Detection and classification of marine vehicles
Typ
Examensarbete för masterexamen
Program
Computer systems and networks (MPCSN), MSc
Publicerad
2021
Författare
Rofalis, Athanasios
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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:
Beskrivning
Ämne/nyckelord
Classification , detection , deep learning , computer vision , YOLO