Multi-scale ship detection algorithm in SAR images in complex scenes

Aiming at the problem of false detection and missing detection in multi-scale SAR ship object detection in complex scenes, an improved SAR ship object detection method is proposed in this paper. Firstly, a multi-scale object feature extraction network (MFE-Net) is used to extract feature information to improve the detection capability of multi-scale objects and reduce redundant calculations. Secondly, deformable convolution (DConv) is introduced to improve the detection performance of SAR ships in complex scenarios by adjusting the shape of the convolution kernel adaptively. Finally, an attention mechanism is introduced to suppress background clutter and enhance feature information. The experimental results show that the detection accuracy of the proposed method on SSDD and HRSID data sets reaches 97.9% and 93.1%, respectively, and the overall performance is better than the existing mainstream object detection algorithms.