Enhancing YOLO-Based SAR Ship Detection with Attention Mechanisms

This study enhances Synthetic Aperture Radar (SAR) ship detection by integrating attention mechanisms, Bi-Level Routing Attention (BRA), Swin Transformer, and a Convolutional Block Attention Module (CBAM) into state-of-the-art YOLO architectures (YOLOv11 and v12). Addressing challenges like small ship sizes and complex maritime backgrounds in SAR imagery, we systematically evaluate the impact of adding and replacing attention layers at strategic positions within the models. Experiments reveal that replacing the original attention layer at position 4 (C3k2 module) with the CBAM in YOLOv12 achieves optimal performance, attaining an mAP@0.5 of 98.0% on the SAR Ship Dataset (SSD), surpassing baseline YOLOv12 (97.8%) and prior works. The optimized CBAM-enhanced YOLOv12 also reduces computational costs (5.9 GFLOPS vs. 6.5 GFLOPS in the baseline). Cross-dataset validation on the SAR Ship Detection Dataset (SSDD) confirms consistent improvements, underscoring the efficacy of targeted attention-layer replacement for SAR-specific challenges. Additionally, tests on the SADD and MSAR datasets demonstrate that this optimization generalizes beyond ship detection, yielding gains in aircraft detection and multi-class SAR object recognition. This work establishes a robust framework for efficient, high-precision maritime surveillance using deep learning.