Ship Detection in SAR Images Using Sparse R-CNN with Wavelet Deformable Convolution and Attention Mechanism

This paper proposes a synthetic aperture radar (SAR) ship detection method based on wavelet-domain deformable convolution (WDC) and multi-head attention, built upon the Sparse R-CNN framework. First, a wavelet-domain convolution module is introduced to enhance the modeling of ship targets with diverse scales and shapes while incorporating frequency-domain information. Deformable convolution adaptively adjusts sampling locations, overcoming the limitations of traditional convolution in capturing target edges and blurred boundaries. Next, a position encoding module is employed to normalize candidate bounding box coordinates and integrate them into region-of-interest features. By providing spatial context, position encoding strengthens spatial perception and enables the subsequent multi-head attention mechanism to more effectively capture associations between targets and candidate regions, thereby improving localization accuracy under arbitrary spatial distributions. Furthermore, the original dynamic head is replaced with a multi-head attention mechanism. Through position-encoded multi-head attention, the model more accurately emphasizes regions with spatial and semantic correlations to the target, enhancing both focus and discrimination for sparse targets. Extensive experiments conducted on two benchmark datasets (SSDD and HRSID) demonstrate the effectiveness and superiority of the proposed method. Overall, the method significantly improves the detection of sparse, multi-scale, and randomly distributed ship targets in SAR images.