Abstract
Given the challenges posed by noise and varying target scales in SAR images, conventional convolutional neural networks often underperform in SAR image detection. To address this, this paper introduces a novel approach. Firstly, a Res-Clo network is proposed for denoising SAR images as a preprocessing step to enhance detection accuracy. Subsequently, an improved network, DML-YOLOv8, is devised based on the YOLOv8 network. The enhancements in the proposed algorithm include several key modifications. Firstly, within the feature extraction layers, a designed MFB module is integrated to effectively broaden the network's receptive field. Next, deformable convolutions are introduced in the feature fusion layers to bolster the network's capability for multi-scale detection. Additionally, a novel loss function, RT-IOU, is designed in feature detection to enhance network inference speed. Finally, a specialized STD small target detection layer is designed to improve detection accuracy for small targets. In practical experiments, it has been shown that the detection method proposed in this paper effectively improves the detection performance of noisy SAR images, and also achieves satisfactory results in multi-scale detection.