  In this paper, we introduce a challenging global large-scale ship database
(called GLSD), designed specifically for ship detection tasks. The designed
GLSD database includes a total of 212,357 annotated instances from 152,576
images. Based on the collected images, we propose 13 ship categories that
widely exist in international routes. These categories include Sailing boat,
Fishing boat, Passenger ship, Warship, General cargo ship, Container ship, Bulk
cargo carrier, Barge, Ore carrier, Speed boat, Canoe, Oil carrier, and Tug. The
motivations of developing GLSD include the following: 1) providing a refine and
extensive ship detection database that benefits the object detection community,
2) establishing a database with exhaustive labels (bounding boxes and ship
class categories) in a uniform classification scheme, and 3) providing a
large-scale ship database with geographic information (covering more than 3000
ports and 33 routes) that benefits multi-modal analysis. In addition, we
discuss the evaluation protocols corresponding to image characteristics in GLSD
and analyze the performance of selected state-of-the-art object detection
algorithms on GSLD, aiming to establish baselines for future studies. More
information regarding the designed GLSD can be found at
https://github.com/jiaming-wang/GLSD.
