YOLO-LDFI: A Lightweight Deformable Feature-Integrated Detector for SAR Ship Detection

A lightweight enhanced detection model named YOLO-LDFI is proposed in this study for ship target detection in SAR images, aiming to improve detection accuracy and deployment efficiency under complex maritime environments. Based on YOLOv11n, the model incorporates four architectural improvements in a progressive manner: linear deformable convolution (LDConv), deformable context-aware attention mechanism (DCAM), frequency-adaptive dilated convolution detection head (FAHead), and Inner-EIoU. Experiments conducted on the public SAR ship detection dataset HRSID demonstrate that the proposed model achieves an AP50 of 90.7% and an F1 score of 87.0%, with only 2.63 M parameters and a computational complexity of 6.7 GFLOPs. Ablation experiments validate the contribution of each component to improved feature alignment, reduced background interference, and more accurate target localization. Overall, the results indicate that the proposed model offers a reasonable trade-off between detection performance and computational efficiency in SAR ship detection tasks.