Appearance- and Orientation-aware Fine-grained Rotated Ship Detection in High-Resolution Satellite Imagery

Abstract

Ship detection using remote sensing imagery is a crucial research area with both military and civilian applications. However, it remains challenging due to limitations in current ship datasets, such as insufficient volume, incomplete annotations, and inaccuracies. Additionally, ships often exhibit arbitrary orientations, dense clustering, varying aspect ratios, and significant dimensional changes. To address these issues, this paper advances ship detection from both data and methodological perspectives. First, a new dataset, ORSISOD, is introduced. This dataset includes seven finely categorized ship types, annotated with rotated bounding boxes, which are more appropriate for ship detection than traditional horizontal boxes. Second, a novel rotated ship detection method is proposed, incorporating a Dynamic IOU Threshold Selection (DITS) module and a Positive Sample Quality Assessment (PSQA) module. DITS adjusts the IOU threshold based on ship size and shape, while PSQA assesses sample quality using ship aspect ratio and angle information. The ORSISOD dataset was tested on 12 object detection algorithms, providing benchmarks for ship detection. Furthermore, the proposed method was evaluated on both ORSISOD and DOTA datasets, demonstrating superior performance.