提升雷达图像船舶检测精度，尤其改善小船和多尺度目标识别
Convolutional Feature Enhancement and Attention Fusion BiFPN for Ship Detection in SAR Images
- 引入卷积增强模块与注意力融合的双向特征金字塔
- 在SSDD数据集上小目标检测准确率显著提升
- 适合遥感图像中复杂背景下小目标检测任务
合成孔径雷达（SAR）通过主动微波和先进信号处理实现亚米级分辨率成像，具备全天候监测能力，已在海上船舶检测等关键领域广泛应用。然而，当前SAR船舶检测面临诸多挑战：船舶尺度差异大、小型近海船只易被噪声干扰、大型近岸船舶受复杂背景影响。为此，本文提出一种新型特征增强与融合框架C-AFBiFPN。该框架在主干网络后构建卷积特征增强（CFE）模块，以丰富特征表示，强化对局部细节与上下文信息的捕捉能力；同时创新性地将BiFormer注意力机制融入BiFPN的融合策略，形成AFBiFPN网络，显著提升跨尺度特征融合的全局建模能力，并能自适应聚焦关键特征区域。在SAR船舶检测数据集（SSDD）上的实验表明，所提方法大幅提升了小目标检测精度、抗遮挡鲁棒性及多尺度适应能力。
原文摘要 · Abstract (English)
Synthetic Aperture Radar (SAR) enables submeter-resolution imaging and all-weather monitoring via active microwave and advanced signal processing. Currently, SAR has found extensive applications in critical maritime domains such as ship detection. However, SAR ship detection faces several challenges, including significant scale variations among ships, the presence of small offshore vessels mixed with noise, and complex backgrounds for large nearshore ships. To address these issues, this paper proposes a novel feature enhancement and fusion framework named C-AFBiFPN. C-AFBiFPN constructs a Convolutional Feature Enhancement (CFE) module following the backbone network, aiming to enrich feature representation and enhance the ability to capture and represent local details and contextual information. Furthermore, C-AFBiFPN innovatively integrates BiFormer attention within the fusion strategy of BiFPN, creating the AFBiFPN network. AFBiFPN improves the global modeling capability of cross-scale feature fusion and can adaptively focus on critical feature regions. The experimental results on SAR Ship Detection Dataset (SSDD) indicate that the proposed approach substantially enhances detection accuracy for small targets, robustness against occlusions, and adaptability to multi-scale features.
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