Detection and classification of marine vehicles

Examensarbete för masterexamen

Använd denna länk för att citera eller länka till detta dokument: https://hdl.handle.net/20.500.12380/304400
Ladda ner:
Fil Beskrivning StorlekFormat 
2021-82 Athanasios Rofalis.pdfMaster Thesis1.67 MBAdobe PDFVisa
Bibliografiska detaljer
FältVärde
Typ: Examensarbete för masterexamen
Titel: Detection and classification of marine vehicles
Författare: Rofalis, Athanasios
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:
Nyckelord: Classification;detection;deep learning;computer vision;YOLO
Utgivningsdatum: 2021
Utgivare: Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper
Serie/rapport nr.: 2021:82
URI: https://hdl.handle.net/20.500.12380/304400
Samling:Examensarbeten för masterexamen // Master Theses



Materialet i Chalmers öppna arkiv är upphovsrättsligt skyddat och får ej användas i kommersiellt syfte!