  Obstacle detection is a fundamental capability of an autonomous maritime
surface vessel (AMSV). State-of-the-art obstacle detection algorithms are based
on convolutional neural networks (CNNs). While CNNs provide higher detection
accuracy and fast detection speed, they require enormous amounts of data for
their training. In particular, the availability of domain-specific datasets is
a challenge for obstacle detection. The difficulty in conducting onsite
experiments limits the collection of maritime datasets. Owing to the logistic
cost of conducting on-site operations, simulation tools provide a safe and
cost-efficient alternative for data collection. In this work, we introduce
SimuShips, a publicly available simulation-based dataset for maritime
environments. Our dataset consists of 9471 high-resolution (1920x1080) images
which include a wide range of obstacle types, atmospheric and illumination
conditions along with occlusion, scale and visible proportion variations. We
provide annotations in the form of bounding boxes. In addition, we conduct
experiments with YOLOv5 to test the viability of simulation data. Our
experiments indicate that the combination of real and simulated images improves
the recall for all classes by 2.9%.
