Dense Representative Points-Guided Rotated-Ship Detection in Remote Sensing Images

Highlights: What are the main findings? Our DenseRRSD method demonstrated exceptional detection accuracy, achieving a mean Average Precision of 91.2% on the HRSC2016 dataset and 83.2% on the DOTA-SHIP dataset. These results validate the model’s high precision in detecting rotated ships. The integration of dense RepPoints representation with the edge sampling strategy, the Weighted Residual Feature Pyramid Network, and the Weighted Chamfer Loss enables robust detection performance even under challenging conditions, such as high object density, arbitrary orientations, and complex backgrounds. What are the implications of the main findings? Dense representative points can significantly improve the precision of detecting geometrically complex objects, not just ships, but also aircraft or buildings in aerial images. The framework of DenseRRSD can be adapted to other object detection and image analysis tasks that require high precision and robustness. Withcontinuous advancements in remote sensing technology, object detection in remote sensing images has emerged as a critical research direction in maritime surveillance, port management, and national defense. Among these applications, ship detection is a key task. Due to the fact that ships in images typically exhibit arbitrary rotations, multi-scale distributions, and complex backgrounds, conventional detection methods based on horizontal or rotated bounding boxes often fail to adequately capture the fine-grained information of the targets, thereby compromising detection accuracy. This paper proposes the Dense Representative Points-Guided Rotated-Ship Detection (DenseRRSD) method. The proposed approach represents ship objects using dense representative points (RepPoints) to effectively capture local semantic information, thereby avoiding the background noise issues associated with traditional rectangular bounding box representations. To further enhance detection accuracy, an edge region sampling strategy is devised to uniformly sample RepPoints from critical ship parts, and a Weighted Residual Feature Pyramid Network (WRFPN) is introduced to efficiently fuse the multi-scale features through residual connections and learnable weights. In addition, a Weighted Chamfer Loss (WCL) combined with a staged localization loss strategy is employed to progressively refine localization from coarse to fine stages. Experimental results on both the HRSC2016 dataset and the newly constructed DOTA-SHIP dataset demonstrate that DenseRRSD achieves state-of-the-art detection accuracy, with mean Average Precision (mAP) scores of 91.2% and 83.2%, respectively, significantly outperforming existing methods. These results verify the effectiveness and robustness of the proposed approach in rotated-ship detection under diverse conditions.