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

By Zhenfeng Shao, Wenjing Wu, Zhongyuan Wang, Wan Du, Chengyuan Li. IEEE transactions on multimedia, 2018

441 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/dcb6f06631021811091ce691592b12a237c12907

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In Computer Vision and Pattern Recognition

  1. Hao Su
  2. Jun Shi
  3. Xiaoling Zhang
  4. Jianwei Li
  5. Shunjun Wei

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