The aim of the project was to analyze the possibility of using machine learning and computer vision to identify (indicate the location) of all sea-going vessels located in the selected area of the open sea and to classify the main attributes of the vessel. The key elements of the project were to download data from the Sentinel-1 satellite [1], download data on the sea vessels [2], then automatically tag data and develop a detection and classification algorithm. The results obtained from the YOLOv7 model on the test set were Mean Average Precision (mAP@.5) = 91% and F1-score = 93% for the single-class ship detection task. When combining the task of ship detection with a ship’s length and width classification, Mean Average Precision for all classes was 40%, f1-score was 41%
Authors
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1109/igarss52108.2023.10283395
- Category
- Aktywność konferencyjna
- Type
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language
- angielski
- Publication year
- 2023
