PPDM-YOLO: A Lightweight Algorithm for SAR Ship Image Target Detection in Complex Environments

To address the critical challenges in Synthetic Aperture Radar (SAR) ship target detection, including complex background speckle noise interference and the difficulty in balancing model lightweight design with detection accuracy, this paper proposes an innovative PPDM-YOLO model. Through modular architecture design, we establish a four-part technical framework: First, a lightweight feature extraction module named PCA is developed to reduce computational complexity by analyzing feature map redundancy, effectively mitigating feature degradation caused by noise. Second, the Noise-Resistant Enhancement Module, PSA-G, integrates the Multi-Scale Adaptive Gradient Threshold (AGT) module with a Dynamic Spatial Attention mechanism. This integration enhances target feature representation while effectively suppressing noise interference. Third, DySample technology is employed in place of conventional upsampling methods to improve the quality of feature reconstruction and preserve spatial details. Additionally, a Multi-Scale Fusion Small Target detection network (MSTFNet) is introduced to boost small object detection through cross-layer feature interaction. Experimental results on HRSID and SSDD datasets demonstrate that PPDM-YOLO achieves 93.7% mAP50 and 70.3% mAP50-95 on HRSID, while reaching 99.4% mAP50 and 78.7% mAP50-95 on SSDD, showing significant advantages over mainstream detection models.With 34.7% fewer parameters than YOLOv11n, our model achieves optimal balance among noise suppression, model lightweighting, and detection accuracy. This research provides an efficient and reliable technical solution for real-time SAR ship detection in complex marine environments.