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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, an open large-scale image dataset for object detection in the maritime domain, is introduced and, to the best of the authors' knowledge, it is by far the largest publicly available dataset for object detection in the maritime domain.

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 and wind farm) for maritime object detection. The dataset has images of 3840$\times$2160 pixels and to our knowledge, it is by far the largest publicly available dataset for object detection in the maritime domain. We performed object detection experiments and evaluated our dataset on several pre-trained state-of-the-art architectures to show the effectiveness and usefulness of our dataset. The dataset is available at: \url{https://github.com/MaritimeDataset/KOLOMVERSE}.

KOLOMVERSE: Korea open large-scale image dataset for object detection in the maritime universe

TL;DR

KOLOMVERSE, an open large-scale image dataset for object detection in the maritime domain, is introduced and, to the best of the authors' knowledge, it is by far the largest publicly available dataset for object detection in the maritime domain.

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 and wind farm) for maritime object detection. The dataset has images of 38402160 pixels and to our knowledge, it is by far the largest publicly available dataset for object detection in the maritime domain. We performed object detection experiments and evaluated our dataset on several pre-trained state-of-the-art architectures to show the effectiveness and usefulness of our dataset. The dataset is available at: \url{https://github.com/MaritimeDataset/KOLOMVERSE}.
Paper Structure (12 sections, 15 figures, 4 tables)

This paper contains 12 sections, 15 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related works
  3. Dataset
  4. Data collection and preprocessing
  5. Data annotation
  6. Data quality assessment
  7. Dataset description
  8. Experimental results and discussion
  9. Implementation details
  10. Experimental settings
  11. Experimental results
  12. Conclusion

Figures (15)

  • Figure 1: Framework of data collection procedure.
  • Figure 2: Left: Twenty-one territorial waters of Korea for data collection. Right: Top view of the ship installed with four-channel 4K NVR system.
  • Figure 3: Left: The distribution of the amount of time taken to collect the video data from 21 territories. Right: Image sample with partially visible and annotated objects.
  • Figure 4: Left: Framework of data quality assessment procedure. Right: Distribution of the train-validation-test split in the 21 territories.
  • Figure 5: Left: Pie chart of class instances in the KOLOMVERSE. Right: Image samples containing objects from the five classes (ship, buoy, fishnet buoy, lighthouse and wind farm).
  • ...and 10 more figures