SOD-Net: A Small Ship Object Detection Network for SAR Images

Abstract

In ship detection using synthetic aperture radar (SAR), small targets and complex background noise remain key challenges that restrict the detection performance. In this letter, we propose a small-target ship detection network based on a small object detection network (SOD-Net) using SAR images. First, we construct a U-shaped feature preextraction network and adopt a spatial pixel attention (SPA) mechanism to enhance the initial feature representation ability. Second, a pinwheel convolution (PC) convolutional neural network (CNN)-based cross-scale feature fusion (CCFF) module is designed. By expanding the receptive field through asymmetric convolution kernels and reducing the parameter scale, features of small targets are properly captured. Evaluation results show that the proposed SOD-Net achieves evaluation accuracies of 98.4% and 91.0% on the benchmark SSDD and HRSID datasets (mean average precision (mAP) at an intersection over union of 0.5), respectively, with only 28 million parameters, thus outperforming state-of-the-art models (e.g., YOLOv8 and D-FINE). Visual analysis confirmed that the SOD-Net is robust in scenarios, including complex sea conditions, dense port berthing, and noise interference, thereby providing an accurate and efficient solution for SAR maritime monitoring.