KOLOMVERSE: Korea Open Large-Scale Image Dataset for Object Detection in the Maritime Universe

Object detection in the maritime domain is crucial for ensuring the safety and navigation of ships. However, there is still a lack of publicly available large-scale datasets in this domain. To address this challenge, we introduce KOLOMVERSE, an open large-scale image dataset for object detection in the maritime domain. We gathered 5,845 hours of video data captured from 21 territorial waters of South Korea. Through rigorous data quality assessment, we manually gathered around 186,419 4K resolution images from the video data. The KOLOMVERSE includes five classes (ship, buoy, fishnet buoy, lighthouse and wind farm) for maritime object detection. The dataset comprises images with dimensions of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3840 \times 2160$ </tex-math></inline-formula> pixels and, to the best of our knowledge, it is by far the largest publicly available dataset for object detection in the maritime domain. We conducted object detection experiments and evaluated our dataset using several state-of-the-art pre-trained architectures to demonstrate its effectiveness and usefulness. The dataset is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/MaritimeDataset/KOLOMVERSE</uri>.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Intelligent Transportation Systems
Published
2024-09-23
DOI
https://doi.org/10.1109/tits.2024.3449122
Citations
8
Primary Topic
Marine and Coastal Research
Type
article
Field-Weighted Citation Impact
2.69

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

KOLOMVERSE: Korea Open Large-Scale Image Dataset for Object Detection in the Maritime Universe

Hyeopwoo Lee, Abhilasha Nanda, Sung Won Cho, Jin Hyoung Park
8 citations
IEEE Transactions on Intelligent Transportation Systems
Marine and Coastal Research
2.69
article

KOLOMVERSE: Korea Open Large-Scale Image Dataset for Object Detection in the Maritime Universe

Hyeopwoo Lee, Abhilasha Nanda, Sung Won Cho, Jin Hyoung Park
article en
8 citations

Abstract

Object detection in the maritime domain is crucial for ensuring the safety and navigation of ships. However, there is still a lack of publicly available large-scale datasets in this domain. To address this challenge, we introduce KOLOMVERSE, an open large-scale image dataset for object detection in the maritime domain. We gathered 5,845 hours of video data captured from 21 territorial waters of South Korea. Through rigorous data quality assessment, we manually gathered around 186,419 4K resolution images from the video data. The KOLOMVERSE includes five classes (ship, buoy, fishnet buoy, lighthouse and wind farm) for maritime object detection. The dataset comprises images with dimensions of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3840 \times 2160$ </tex-math></inline-formula> pixels and, to the best of our knowledge, it is by far the largest publicly available dataset for object detection in the maritime domain. We conducted object detection experiments and evaluated our dataset using several state-of-the-art pre-trained architectures to demonstrate its effectiveness and usefulness. The dataset is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/MaritimeDataset/KOLOMVERSE</uri>.

IEEE Transactions on Intelligent Transportation SystemsVol. 25(12)
Dankook University (KR)
Ministry of Oceans and Fisheries, Ministry of Science and ICT, South Korea
Openalex Percentile: Top 11%
Marine and Coastal Research
2.69
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.