Abstract: To address the insufficient cross-domain generalization ability of existing ship target detection models and their poor detection stability under extreme noise and complex sea surface conditions in Synthetic Aperture Radar (SAR) imagery, this paper proposes an improved ship target detection algorithm, CK-YOLO, based on YOLOv12. The proposed method aims to enhance the model’s robustness and adaptability in SAR data. First, to improve the extraction of ship boundary features and strengthen contextual modeling capability, an SKC3k2 module is designed. This module enhances boundary feature representation by incorporating a Kolmogorov–Arnold Network (KAN) layer with residual connections into the original C2k2 structure, and introduces a switchable atrous convolution（SAConv） mechanism to adaptively adjust the receptive field for better multi-scale feature extraction. Furthermore, to improve the model’s dynamic modeling capacity and its ability to extract high-level semantic information, a CST module is developed. The CST module consists of a local convolution branch for spatial modeling and a sparse dynamic branch based on a Liquid Neural Network (LNN), which leverages temporal modeling advantages to enhance high-order semantic feature extraction. To validate the effectiveness of the proposed method, experiments were conducted on SAR datasets provided by the China Centre for Resource Satellite Data and Application and the LS-SSDD dataset. The results demonstrate that model A achieves improvements of 0.8% and 1.3% in mAP@50 over YOLOv12n, respectively, thereby exhibiting the best performance among all compared models. In addition, cross-domain generalization experiments using the LS-SSDD and MMShip datasets demonstrate that CK-YOLO achieves the best overall performance among the YOLO series models, showing superior robustness and generalization ability in both intra-domain SAR detection and cross-modal detection tasks. Finally, ablation studies further confirm the effectiveness and contribution of the proposed modules. The CK-YOLO model maintains a lightweight architecture while effectively reducing missed detections and false alarms in SAR images with strong noise and complex sea surface conditions.

摘要： 针对现有船舶目标检测模型在跨域场景下泛化能力不足，以及在合成孔径雷达（Synthetic Aperture Radar，SAR）图像中面对极端噪声与复杂海面环境时检测稳定性较差的问题，提出了一种基于YOLOv12改进的船舶目标检测算法CK-YOLO，以提升模型在SAR数据中的鲁棒性与适应性。首先，为提高模型对船舶边界特征的提取并增强上下文建模能力，设计了SKC3k2模块，通过在C2k2模块中增加KAN（Kolmogorov-Arnold network）层的残差连接增强模型对船舶边界特征的建模能力，同时结合可切换空洞卷积（SAConv）的自适应感受野机制加强对多尺度船舶特征的提取。此外，为提高模型的动态建模能力与高阶语义信息的提取能力，设计了CST模块，CST模块包含空间建模路径的局部卷积分支与基于液态神经网络（Liquid Neural Network，LNN）的稀疏动态分支，用时序建模优势增强高阶语义信息提取能力。为验证改进模型的有效性，使用中国资源卫星应用中心提供的SAR数据集和LS-SSDD数据集对CK-YOLO与主流模型进行了对比实验，结果表明CK-YOLO的mAP@50相比YOLOv12n在mAP@50上分别提高了0.8%和1.3%，在对比模型中表现最优；同时结合LS-SSDD数据集与MMShip数据集对CK-YOLO进行了泛化实验，结果表明改进模型在YOLO系列模型中的综合表现最优，体现出CK-YOLO在SAR域内检测以及在跨模态检测中具备较好的泛化能力与鲁棒性；最后通过模块的消融实验进一步验证了CK-YOLO的有效性及贡献。CK-YOLO模型在保持轻量化同时有效降低了在包含噪声与复杂场景SAR图像中的漏检、误检等问题。