Open Water Detection for Autonomous In-harbor Navigation Using a Classification Network

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Abstract

Autonomous navigation quay to quay is a goal for various surface vessel trades, from inland ferries to river transport and offshore services. Ability to navigate safely within a harbour or other confined waters is an essential step-stone towards this goal. This paper aims at creating a map of open water area that is available for safe navigation, given dynamic and static obstacles. Employing electro-optical sensors, the paper suggests open water detection using a classification convolutional neural network on context sensitive sub-partitioning of an image in a pyramid of smaller areas, combining the classifications in to a map of subareas containing open water. A salient feature of this approach is the ease of annotation and ease of creating a large amount of annotated images that is needed for machine learning. Following classification of sub-areas, camera images are transformed to bird’s view by projective geometry methods to enable planning of feasible paths for navigation. This new approach is validated on data from sea trials in Danish waters
Original languageEnglish
Book seriesIFAC-PapersOnLine
Volume54
Issue number16
Pages (from-to)30–36
ISSN2405-8963
DOIs
Publication statusPublished - 2021
Event13th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles - Online event
Duration: 22 Sep 202124 Sep 2021
Conference number: 13
https://cams-2021.com

Conference

Conference13th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles
Number13
LocationOnline event
Period22/09/202124/09/2021
Internet address

Keywords

  • Deep learning
  • Detection performance
  • Open water detection
  • Computer vision
  • Autonomous Marine Vehicles
  • In-harbor navigation

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