SimuShips - A High Resolution Simulation Dataset for Ship Detection with Precise Annotations

Minahil Raza, Hanna Prokopova, Samir Huseynzade, Sepinoud Azimi, Sebastien Lafond

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

Obstacle detection is a fundamental capability of an autonomous maritime surface vessel (AMSV). State-of-the-art obstacle detection algorithms are based on convolutional neural networks (CNNs). While CNNs provide higher detection accuracy and fast detection speed, they require enormous amounts of data for their training. In particular, the availability of domain-specific datasets is a challenge for obstacle detection. The difficulty in conducting onsite experiments limits the collection of maritime datasets. Owing to the logistic cost of conducting on-site operations, simulation tools provide a safe and cost-efficient alternative for data collection. In this work, we introduce SimuShips, a publicly available simulation-based dataset for maritime environments. Our dataset consists of 9471 high-resolution (1920x1080) images which include a wide range of obstacle types, atmospheric and illumination conditions along with occlusion, scale and visible proportion variations. We provide annotations in the form of bounding boxes. In addition, we conduct experiments with YOLOv5 to test the viability of simulation data. Our experiments indicate that the combination of real and simulated images improves the recall for all classes by 2.9%.
Original languageEnglish
Title of host publicationOCEANS 2022 Hampton Roads
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)9781665468091
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event2022 OCEANS Hampton Roads, OCEANS 2022 - Hampton Roads, United States
Duration: 17 Oct 202220 Oct 2022

Publication series

NameOceans Conference Record (IEEE)
Volume2022-October
ISSN (Print)0197-7385

Conference

Conference2022 OCEANS Hampton Roads, OCEANS 2022
Country/TerritoryUnited States
CityHampton Roads
Period17/10/2220/10/22

Keywords

  • bounding box annotation
  • deep learning based object detectors
  • digital twin
  • maritime vessel dataset
  • object detection
  • ship detection

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