Abstract
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.
Abstract (translated)
合成孔径雷达（SAR）通过主动微波和先进信号处理技术实现了亚米级分辨率成像及全天候监测。目前，SAR在诸如船舶检测等关键海洋领域得到了广泛应用。然而，SAR船舶检测面临着若干挑战，包括船型大小不一、海上小型船只混杂以及近岸大型船只的复杂背景环境。为解决这些问题，本文提出了一种名为C-AFBiFPN的新颖特征增强与融合框架。该框架在骨干网络之后构建了卷积特征增强（CFE）模块，旨在丰富特征表示，并提高捕捉和表达局部细节及上下文信息的能力。 此外，C-AFBiFPN创新性地将BiFormer注意力机制整合到了BiFPN的融合策略中，形成了AFBiFPN网络。这种新方法提升了跨尺度特征融合的全局建模能力，并能自适应聚焦于关键特征区域。在SAR Ship Detection Dataset（SSDD）上的实验结果表明，所提出的方案显著提高了对小目标检测精度、遮挡情况下的鲁棒性以及多尺度特征的适应性。
URL
https://arxiv.org/abs/2506.15231
