Synthetic Aperture Radar (SAR) images are widely used in ship detection because of their all-weather and all-day imaging characteristics. However, there are two challenges for SAR ship detection. One is coherent speckle noise, causing ship confusion with similar objects and raising false alarms. The other is multi-scale ship detection, particularly in small ships, which suffers from insufficient accuracy. To address these challenges, this paper proposes a robust one-stage detector, S3MDet, for SAR ship detection with sequential three-way decisions (S3WDs) and multi-granularity. First, to effectively eliminate the interference of coherent speckle noise, a noise classification and denoising module (NCDM) S3WD-based is designed. This module can accurately classify the noise level of the image and only perform denoising on the images identified as noisy, avoiding unnecessary operations on noise-free images. Then, to solve the problem of multi-scale ship detection, a multi-granularity group attention module (MGAM) is designed to obtain a richer representation of multi-granularity features. This module adopts a multi-granularity group convolution structure and channel-wise attention weights to efficiently extract ship features of different scales from SAR images. Extensive experiments on four SAR ship datasets, including SAR-Ship-Dataset, HRSID, SSDD, and LS-SSDD-v1.0, validate the robustness of S3MDet, demonstrating that it achieves state-of-the-art performance.

中文翻译：

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一种强大的单级探测器，用于 SAR 船舶检测，具有顺序三向决策和多粒度

合成孔径雷达（SAR）图像因其全天候、全天时的成像特点而广泛应用于船舶检测。然而，SAR 船舶检测面临两个挑战。一是相干散斑噪声，导致船舶与类似物体混淆并引发误报。另一个是多尺度船舶检测，尤其是小型船舶，精度不够。为了应对这些挑战，本文提出了一种强大的单级探测器 S3MDet，用于具有顺序三向决策 (S3WD) 和多粒度的 SAR 船舶检测。首先，为了有效消除相干散斑噪声的干扰，设计了一种基于S3WD的噪声分类和去噪模块（NCDM）。该模块可以准确地对图像的噪声级别进行分类，并且只对识别为有噪声的图像进行去噪，避免了对无噪声图像的不必要的操作。然后，为了解决多尺度船舶检测问题，设计了多粒度群体注意力模块（MGAM）以获得更丰富的多粒度特征表示。该模块采用多粒度组卷积结构和通道注意力权重，有效地从SAR图像中提取不同尺度的船舶特征。对四个 SAR 船舶数据集（包括 SAR-Ship-Dataset、HRSID、SSDD 和 LS-SSDD-v1.0）的大量实验验证了 S3MDet 的鲁棒性，证明其实现了最先进的性能。