Objective Instance segmentation of ships plays a crucial role in tasks such as monitoring, identification, and tracking, thereby supporting intelligent navigation. However, the wide variability in ship shapes and scales, coupled with environmental interference, leads to poor performance of existing methods perform in Ship Contour extraction. To address this issue, this paper proposes a novel Ship Contour method based on curve recursion.

Method  By enhancing CenterNet with hierarchical feature extraction and integrating the Deep Layer Aggregation-60 backbone network, the proposed method achieves a balance between accuracy and speed. The Block structure is optimized, and an ECA channel attention mechanism is incorporated to strengthen feature extraction, while the Mish activation function replaces ReLU to improve adaptability in deep learning. In addition, a translation-invariant contour deformation method and a dynamic matching loss function are introduced to accelerate the final contour extraction.

Results  On the dedicated 2023Ship-seg dataset containing 2300 samples, the proposed method achieved an average precision of AP_0.5∶0.95 = 64.0% and a recall rate of AR_0.5∶0.95 = 67.9%, outperforming all mainstream instance segmentation algorithms.

Conclusion  The method can significantly improve visual processing performance in ship monitoring and intelligent navigation scenarios.