Detection and classification of marine vehicles

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/304400
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dc.contributor.authorRofalis, Athanasios-
dc.contributor.departmentChalmers tekniska högskola / Institutionen för mekanik och maritima vetenskapersv
dc.date.accessioned2021-12-10T12:38:37Z-
dc.date.available2021-12-10T12:38:37Z-
dc.date.issued2021sv
dc.date.submitted2020-
dc.identifier.urihttps://hdl.handle.net/20.500.12380/304400-
dc.description.abstractOne 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.language.isoengsv
dc.relation.ispartofseries2021:82sv
dc.setspec.uppsokTechnology-
dc.subjectClassificationsv
dc.subjectdetectionsv
dc.subjectdeep learningsv
dc.subjectcomputer visionsv
dc.subjectYOLOsv
dc.titleDetection and classification of marine vehiclessv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH-
dc.contributor.examinerBenderius, Ola-
dc.contributor.supervisorBenderius, Ola-
dc.identifier.coursecodeMMSX30sv
Collection:Examensarbeten för masterexamen // Master Theses



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