SAR Ship Detection Algorithm Based on Deep Dense Sim Attention Mechanism Network

Abstract

Ship detection is of great significance in the interpretation of synthetic aperture radar (SAR) images. However, SAR generates inherent speckle noise when producing images, which poses many challenges for ship detection tasks. One major issue during detection is low accuracy caused by noise interference near the ship. To address this issue, this study aims to design a deep dense attention detection network for improving the accuracy of ship target detection in SAR. The proposed algorithm primarily uses a multilayer deep dense network to preliminarily extract ship image features, and subsequently introduces an attention network to further enhance these features. Finally, an anchor point mechanism is utilized to perform ship positioning regression estimation. Experimental results on public SAR ship datasets, including SAR ship detection dataset (SSDD) and SAR-Ship-Dataset, demonstrate that the proposed algorithm performs well in terms of speed and accuracy, and has better robustness and real-time performance compared to similar detection algorithms.