Detection and classification of marine vehicles
| dc.contributor.author | Rofalis, Athanasios | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper | sv |
| dc.contributor.examiner | Benderius, Ola | |
| dc.contributor.supervisor | Benderius, Ola | |
| dc.date.accessioned | 2021-12-10T12:38:37Z | |
| dc.date.available | 2021-12-10T12:38:37Z | |
| dc.date.issued | 2021 | sv |
| dc.date.submitted | 2020 | |
| dc.description.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: | sv |
| dc.identifier.coursecode | MMSX30 | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/304400 | |
| dc.language.iso | eng | sv |
| dc.relation.ispartofseries | 2021:82 | sv |
| dc.setspec.uppsok | Technology | |
| dc.subject | Classification | sv |
| dc.subject | detection | sv |
| dc.subject | deep learning | sv |
| dc.subject | computer vision | sv |
| dc.subject | YOLO | sv |
| dc.title | Detection and classification of marine vehicles | sv |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.uppsok | H | |
| local.programme | Computer systems and networks (MPCSN), MSc |