Maritime Semantic Segmentation Training Dataset

MaSTr1325 is a new large-scale marine semantic segmentation training dataset tailored for development of obstacle detection methods in small-sized coastal USVs. The dataset contains 1325 diverse images captured over a two year span with a real USV, covering a range of realistic conditions encountered in a coastal surveillance task. All images are per-pixel semantically labeled and synchronized with inertial measurements of the on-board sensors. In addition, a dataset augmentation protocol is proposed to address slight appearance differences of the images in the training set and those in deployment. This page includes:

Overview

Over the course of two years, the USV was manually guided in the gulf of Koper, Slovenia, covering a range of realistic conditions typical for coastal surveillance and encountering a large variety of obstacles. From more than fifty hours of footage acquired, we have hand-picked representative images of a marine environment. A particular attention was paid to include various weather conditions and times of day to ensure the variety of the captured dataset. Finally, 1325 images were accepted for the final dataset. Images in the dataset are time-synchronized with measurements of the on-board GPS and IMU.

The obstacles in the dataset cover a wide range of shapes and sizes. Some of the obstacles present in the dataset are:

• cargo ships,
• sail boats,
• portable piers,
• swimmers,
• rowers,
• bouys,
• seagulls, …

Due to the nature of the task, the majority of pixels in the dataset correspond to the water or sky component. However, a considerable number of obstacle pixels are still present in the dataset.

Labeling

Each image from the dataset was manually annotated by human annotators with three categories (sea, sky and environment). An image editing software supporting multiple layers per image was used and each semantic region was annotated in a separate layer by multiple sized brushes for speed and accuracy. All annotations were carried out by in-house annotators and were verified and corrected by an expert to ensure a high-grade per-pixel annotation quality. The annotation procedure and the quality control took approximately twenty minutes per image. To account for the annotation uncertainty at the edge of semantically different regions, the edges between these regions were labeled by the unknown category. This label ensures that these pixels are excluded from learning.

Labels in ground-truth annotation masks correspond to the following values:

• Obstacles and environment = 0 (value zero)
• Water = 1 (value one)
• Sky = 2 (value two)
• Ignore region / unknown category = 4 (value four)

Download links

Here is a link to the page containing dataset files and other useful technical information. The page also contains a short video presentation of the dataset.

Citing MaSTr1325

@inproceedings{bb_iros_2019,
  title={The MaSTr1325 dataset for training deep USV obstacle detection models},
  author={Bovcon, Borja and Muhovi{\v{c}}, Jon and Per{\v{s}}, Janez and Kristan, Matej},
  booktitle={2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2019},
  organization={IEEE}
}