SV-DAPNet: An Enhanced Faster R-CNN Network for Object Detection in SAR Images

Abstract

The synthetic aperture radar (SAR) object detection is crucial for military reconnaissance and environmental monitoring. However, the existing methods often struggle to maintain high accuracy in complex scenarios due to severe speckle noise, large variations in target scale, and similar feature interference. To address these challenges, this letter proposes SV-DAPNet, a robust detection framework based on the faster R-CNN architecture. We introduce three key innovations: 1) spatial variance modulation (SVM), which quantifies spatial variance to suppress speckle noise and enhance target features adaptively, 2) dynamic atrous spatial pyramid pooling (DASPP), which dynamically fuses multiscale features to handle large-scale variations, and 3) dynamic prototype contrastive learning (DPCL), which optimizes feature distribution to improve intraclass compactness and interclass discrimination. Extensive experiments on the SAR-AIRcraft-1.0 aircraft dataset and HRSID ship dataset demonstrate that SV-DAPNet achieves 89.6% mAP@0.5 and 49.7% mAP@0.5:0.95 on SAR-AIRcraft-1.0, outperforming the state-of-the-art baselines, and delivers consistent performance gains on cross-dataset validation with reasonable parameter consumption.