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Swin-PAFF: A SAR Ship Detection Network with Contextual Cross-Information Fusion

Yujun Zhang*, Dezhi Han, Peng Chen

School of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China

* Corresponding Author: Yujun Zhang. Email: email

Computers, Materials & Continua 2023, 77(2), 2657-2675.


Synthetic Aperture Radar (SAR) image target detection has widespread applications in both military and civil domains. However, SAR images pose challenges due to strong scattering, indistinct edge contours, multi-scale representation, sparsity, and severe background interference, which make the existing target detection methods in low accuracy. To address this issue, this paper proposes a multi-scale fusion framework (Swin-PAFF) for SAR target detection that utilizes the global context perception capability of the Transformer and the multi-layer feature fusion learning ability of the feature pyramid structure (FPN). Firstly, to tackle the issue of inadequate perceptual image context information in SAR target detection, we propose an end-to-end SAR target detection network with the Transformer structure as the backbone. Furthermore, we enhance the ability of the Swin Transformer to acquire contextual features and cross-information by incorporating a Swin-CC backbone network model that combines the Spatial Depthwise Pooling (SDP) module and the self-attentive mechanism. Finally, we design a cross-layer fusion neck module (PAFF) that better handles multi-scale variations and complex situations (such as sparsity, background interference, etc.). Our devised approach yields a noteworthy AP@0.5:0.95 performance of 91.3% when assessed on the HRSID dataset. The application of our proposed technique has resulted in a noteworthy advancement of 8% in the AP@0.5:0.95 scores on the HRSID dataset.


Cite This Article

Y. Zhang, D. Han and P. Chen, "Swin-paff: a sar ship detection network with contextual cross-information fusion," Computers, Materials & Continua, vol. 77, no.2, pp. 2657–2675, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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