Synthetic aperture radar (SAR) is instrumental in ship monitoring owing to its all-weather capabilities and high resolution. In SAR images, ship targets frequently display blurred or mixed boundaries with the background, and instances of occlusion or partial occlusion may occur. Additionally, multi-scale transformations and small-target ships pose challenges for ship detection. To tackle these challenges, we propose a novel SAR ship detection model, FESAR. Firstly, in addressing multi-scale transformations in ship detection, we propose the Fused Convolution Enhancement Module (FCEM). This network incorporates distinct convolutional branches designed to capture local and global features, which are subsequently fused and enhanced. Secondly, a Spatial Relationship Analysis Module (SRAM) with a spatial-mixing layer is designed to analyze the local spatial relationship between the ship target and the background, effectively combining local information to discern feature distinctions between the ship target and the background. Finally, a new backbone network, SPD-YOLO, is designed to perform deep downsampling for the comprehensive extraction of semantic information related to ships. To validate the model’s performance, an extensive series of experiments was conducted on the public datasets HRSID, LS-SSDD-v1.0, and SSDD. The results demonstrate the outstanding performance of the proposed FESAR model compared to numerous state-of-the-art (SOTA) models. Relative to the baseline model, FESAR exhibits an improvement in mAP by 2.6% on the HRSID dataset, 5.5% on LS-SSDD-v1.0, and 0.2% on the SSDD dataset. In comparison with numerous SAR ship detection models, FESAR demonstrates superior comprehensive performance.

合成孔径雷达（SAR）因其全天候能力和高分辨率而在船舶监测中发挥着重要作用。在SAR图像中，船舶目标经常与背景显示模糊或混合的边界，并且可能出现遮挡或部分遮挡的情况。此外，多尺度变换和小目标船舶给船舶检测带来了挑战。为了应对这些挑战，我们提出了一种新颖的 SAR 船舶检测模型 FESAR。首先，为了解决船舶检测中的多尺度转换，我们提出了融合卷积增强模块（FCEM）。该网络包含不同的卷积分支，旨在捕获局部和全局特征，然后将其融合和增强。其次，设计了具有空间混合层的空间关系分析模块（SRAM）来分析船舶目标与背景之间的局部空间关系，有效地结合局部信息来辨别船舶目标与背景之间的特征区别。最后，设计了一个新的骨干网络SPD-YOLO来进行深度下采样，以全面提取与船舶相关的语义信息。为了验证模型的性能，在公共数据集 HRSID、LS-SSDD-v1.0 和 SSDD 上进行了一系列广泛的实验。结果表明，与众多最先进的 (SOTA) 模型相比，所提出的 FESAR 模型具有出色的性能。相对于基线模型，FESAR 在 HRSID 数据集上的 mAP 提高了 2.6%，在 LS-SSDD-v1.0 上提高了 5.5%，在 SSDD 数据集上提高了 0.2%。与众多SAR船舶检测模型相比，FESAR表现出优越的综合性能。