Ship Detection Image Dataset

The dataset used in this study consists of 6,468 images collected from multiple publicly available internet resources, including Roboflow public datasets, Kaggle image repositories, the Airbus Ship Detection Challenge dataset, Google image search, and additional images obtained by contacting authors of relevant published studies.

All images were manually annotated and labeled using two classes: Object (1) and Background (0). The Object class represents any visible vessel present in the scene, while Background indicates images containing only sea surface without detectable ships.

The dataset includes images captured from Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs) under diverse environmental conditions. These conditions include different weather scenarios, varying illumination levels, and both daytime and nighttime observations. The images contain multiple vessel categories such as cargo ships, passenger ships, fishing vessels, and other maritime vessels.

The purpose of constructing this dataset is to support the development and evaluation of robust maritime object detection models capable of identifying ships on sea surfaces under realistic operational conditions. The diversity of viewpoints, lighting conditions, and vessel types ensures that the dataset reflects real-world maritime monitoring environments and improves the generalization capability of the trained models.