TY  - EJOU
AU  - Zhang, Yujun 
AU  - Han, Dezhi 
AU  - Chen, Peng 

TI  - Swin-PAFF: A SAR Ship Detection Network with Contextual Cross-Information Fusion
T2  - Computers, Materials \& Continua

PY  - 2023
VL  - 77
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Transformer; deep learning; SAR object detection; ship detection

DO  - 10.32604/cmc.2023.042311