Convolutional Feature Enhancement and Attention Fusion BiFPN for Ship Detection in SAR Images

TL;DR

This paper introduces C-AFBiFPN, a novel feature enhancement and attention fusion framework that significantly improves ship detection accuracy in SAR images, especially for small and multi-scale targets, by enriching feature representation and adaptive focus.

Contribution

It proposes a new C-AFBiFPN framework combining convolutional feature enhancement and BiFormer attention for improved SAR ship detection.

Findings

Enhanced detection accuracy for small ships

Improved robustness against occlusions

Better multi-scale feature adaptation

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.