CA-CFAR Soft Attention and Scatter-Aware Context Aggregation for Small Ship Detection in SAR Imagery

Small ship detection in synthetic aperture radar (SAR) imagery is limited by feature loss at coarse strides, sea-clutter false alarms, and the sensitivity of small boxes to annotation offsets. We propose CaSA-DETR, a detector built on RT-DETRv2 with three components. CFAR-Attention is a CFAR-inspired local contrast normalisation that reweights the stride-8 and stride-16 features bypassing the Transformer encoder, using sliding-window background statistics. Scatter-Aware Context Aggregation (SACA) collects context at four dilation rates with parallel dilated depthwise-separable convolutions and branch-adaptive gating. Inner-GIoU computes the regression loss on the inner 49% of each box, intended to reduce the influence of boundary offsets on targets smaller than 32 by 32 pixels. On a benchmark of 44,217 training images (73,407 instances, all under 32 by 32 pixels), the full model with a ResNet-101-vd backbone at 1024 by 1024 input reaches an average precision (AP) of 60.75% and an F1 score of 90.10%, 3.96 AP above D-FINE at the same input size. A cross of backbone, input size and modules shows that most of the gain over the original RT-DETRv2 baseline comes from the higher input, and that the three modules add 1.0 to 1.4 AP at every backbone and input size tested. On 2244 ship-free images, the modules cut false alarms by about 30% at a confidence of 0.5, at a cost of 0.66% more parameters and 2.3% more floating-point operations (FLOPs). The three modules improve AP at low cost.