SeaShips: A Large-Scale Precisely Annotated Dataset for Ship Detection

TLDR

This paper introduces SeaShips, a large‑scale ship detection dataset, and establishes baseline detector performance to benchmark future research. SeaShips contains 31,455 images from 10,080 real‑world video segments, covering six ship types with diverse scales, viewpoints, and illumination, each annotated with precise bounding boxes. Baseline experiments on SeaShips reveal detection challenges and provide comparative results, demonstrating the dataset’s potential to advance ship‑detection research.

Abstract

In this paper, we introduce a new large-scale dataset of ships, called SeaShips, which is designed for training and evaluating ship object detection algorithms. The dataset currently consists of 31 455 images and covers six common ship types (ore carrier, bulk cargo carrier, general cargo ship, container ship, fishing boat, and passenger ship). All of the images are from about 10 080 real-world video segments, which are acquired by the monitoring cameras in a deployed coastline video surveillance system. They are carefully selected to mostly cover all possible imaging variations, for example, different scales, hull parts, illumination, viewpoints, backgrounds, and occlusions. All images are annotated with ship-type labels and high-precision bounding boxes. Based on the SeaShips dataset, we present the performance of three detectors as a baseline to do the following: 1) elementarily summarize the difficulties of the dataset for ship detection; 2) show detection results for researchers using the dataset; and 3) make a comparison to identify the strengths and weaknesses of the baseline algorithms. In practice, the SeaShips dataset would hopefully advance research and applications on ship detection.