Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Research.fi
Dataset . 2024
License: CC BY
Data sources: Research.fi
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Research.fi
Dataset . 2024
License: CC BY
Data sources: Research.fi
versions View all 5 versions
addClaim

ABOships-PLUS

Authors: Winsten Jesper; Iancu Bogdan; Soloviev Valentin; Lilius Johan;
Abstract

ABOships-PLUS is an improved iteration of the original ABOships dataset. It includes 9,880 images capturing maritime scenes, showcasing various types of maritime objects such as powerboats, ships, sailboats, and stationary objects. Detailed category definitions and images can be found in the associated reference paper. In total, ABOships-PLUS contains 33,227 annotated objects across these categories, including four types of ships. Several key changes and improvements have been made to ABOships-PLUS: Object Size Filtering: In ABOships-PLUS, a filtering process was applied to exclude very small objects, specifically those with an occupied pixel area less than 16^2x16^2 pixels. This filtering ensures that the dataset primarily consists of more discernible maritime objects, contributing to improved data quality. Superclass Aggregation: A notable transformation in ABOships-PLUS is the grouping of objects into four superclasses based on their distinct visual characteristics. This superclass aggregation facilitates the use of both transfer learning and learning from scratch, making the dataset more versatile for various machine learning applications. Semantic Relevance: The categorization into superclasses in ABOships-PLUS was guided by semantic relevance, with human supervision. The objective was to create more meaningful superclasses, both from a semantic and visual perspective, enhancing the dataset's utility for maritime object detection research. Format Transition: A significant change occurred in the data format. ABOships-PLUS adopts the COCO format for object detection: https://cocodataset.org/#format-data. This format transition enhances compatibility with a broader range of machine learning frameworks and tools, in contrast to the original ABOships dataset, which used CSV format. To create ABOships-PLUS, images were extracted from videos recorded in MPEG format, with a resolution of 720p at 15 frames per second (FPS). An image was extracted every 15 seconds, equivalent to every 225 frames, from videos filmed in the Finnish Archipelago using a camera attached to a moving watercraft known as a waterbus or "vesibussi" in Finnish. The distribution of labels within ABOships-PLUS is as follows: powerboat (21.8%), ship (46.0%), sailboat (24.2%), and stationary objects (8.1%). These changes aim to enhance the dataset's usability for maritime object detection research and applications. Reference article: https://doi.org/10.3390/jmse11091638

Country
Finland
Related Organizations
Keywords

maritime imagery, maritime vessel dataset, ship detection, maritime vessel detection, autonomous maritime navigation, ship dataset

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
    OpenAIRE UsageCounts
    Usage byUsageCounts
    visibility views 16
    download downloads 3
  • 16
    views
    3
    downloads
    Powered byOpenAIRE UsageCounts
Powered by OpenAIRE graph
Found an issue? Give us feedback
visibility
download
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
0
Average
Average
Average
16
3