SLPDR: A Benchmark for Ship License Plate Detection and Recognition

Youmei Zhang, Junyu Chen, Ran Song, Yonghuai Liu, Ardhendu Behera, Mingxin Zhang, Wei Zhang

Abstract: Ship identification is a prerequisite for the intelligent management of maritime transportation, yet existing research is confined to broad ship detection and categorization, which only provides the ship’s location or type instead of its identification. Inspired by the research on the Car License Plate (CLP), we make the first attempt to propose the concept of the Ship License Plate (SLP). In addition, the limited data hinders research on ship identification. To overcome this obstacle, we construct the first large-scale Ship License Plate Detection and Recognition (SLPDR) dataset, which contains 1,472 ship identities and 88,862 images. In addition, this paper proposes an SLP detection model named YOLO-SSA and evaluates this model as well as typical detection methods on the SLPDR dataset. The experimental results demonstrate that the proposed YOLO-SSA achieves better SLP detection performance by enhancing the features where ships and SLPs are located. Furthermore, we explore the prospective applications of SLPs in intelligent maritime transportation, including ship monitoring and berth management. Project web page: https://vsislab.github.io/SLPDR/

External IDs:doi:10.1109/tits.2025.3590177