@Article{cmc.2023.042311,
AUTHOR = {Yujun Zhang, Dezhi Han, Peng Chen},
TITLE = {Swin-PAFF: A SAR Ship Detection Network with Contextual Cross-Information Fusion},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {77},
YEAR = {2023},
NUMBER = {2},
PAGES = {2657--2675},
URL = {http://www.techscience.com/cmc/v77n2/54819},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2023.042311}
}