KOLOMVERSE: Korea open large-scale image dataset for object detection in the maritime universe
Abhilasha Nanda, Sung Won Cho, Hyeopwoo Lee, Jin Hyoung Park

TL;DR
KOLOMVERSE is the largest publicly available large-scale image dataset for maritime object detection, capturing diverse environmental conditions and five key classes, facilitating advancements in maritime safety and navigation.
Contribution
This paper introduces KOLOMVERSE, a comprehensive large-scale maritime image dataset with diverse conditions, filling a critical gap in publicly available data for maritime object detection.
Findings
Effective dataset for training maritime object detection models
High diversity in environmental conditions enhances model robustness
Benchmark results demonstrate dataset's usefulness for state-of-the-art architectures
Abstract
Over the years, datasets have been developed for various object detection tasks. Object detection in the maritime domain is essential for the safety and navigation of ships. However, there is still a lack of publicly available large-scale datasets in the maritime domain. To overcome this challenge, we present KOLOMVERSE, an open large-scale image dataset for object detection in the maritime domain by KRISO (Korea Research Institute of Ships and Ocean Engineering). We collected 5,845 hours of video data captured from 21 territorial waters of South Korea. Through an elaborate data quality assessment process, we gathered around 2,151,470 4K resolution images from the video data. This dataset considers various environments: weather, time, illumination, occlusion, viewpoint, background, wind speed, and visibility. The KOLOMVERSE consists of five classes (ship, buoy, fishnet buoy, lighthouse…
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Taxonomy
TopicsMaritime Navigation and Safety · Advanced Neural Network Applications · Oil Spill Detection and Mitigation
