Bolstering Maritime Object Detection with Synthetic Data

Jonathan Binner Becktor, Frederik Emil Thorsson Saabye Schöller, Evangelos Boukas, Mogens Blanke, Lazaros Nalpantidis

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Abstract

For autonomy in the maritime domain, object detection is a very important task, as one needs to perceive the surroundings to take appropriate action decisions. A large issue in maritime object detection and classification is the shortage of thorough datasets. In this work, our aim is to reduce this problem by introducing a pipeline for the generation of simulated data that matches the target domain, thereby achieving a more reliable and robust performance of our object detector. This data generation pipeline is easily modifiable and allows for varying setups that would be hard or dangerous to collect in real life. Furthermore, it enables the introduction of new classes without issue.
Original languageEnglish
Book seriesIFAC-PapersOnLine
Volume55
Issue number31
Pages (from-to)64-69
ISSN2405-8963
DOIs
Publication statusPublished - 2022
Event14th IFAC Conference on Control Applications in Marine Systems, Robotics and Vehicles - Technical University of Denmark, Kongens Lyngby, Denmark
Duration: 14 Sept 202216 Sept 2022
https://www.ifac-cams2022.dk/

Conference

Conference14th IFAC Conference on Control Applications in Marine Systems, Robotics and Vehicles
LocationTechnical University of Denmark
Country/TerritoryDenmark
CityKongens Lyngby
Period14/09/202216/09/2022
Internet address

Keywords

  • Autonomous Marine Vessels
  • Machine Learning
  • Autonomous Navigation
  • Simulated data
  • Unreal Engine

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