LGNet: A Lightweight Ghost-Enhanced Network for Efficient SAR Ship Detection
Jiawei Chen, Junyu Huang, Yuna Tan, Zhifeng Wu, Renbo Luo · Remote Sensing · 2025
Current SAR ship detection methods face a critical trade-off between accuracy and computational efficiency, severely limiting their deployment on resource-constrained edge devices that are essential for distributed maritime surveillance systems. This paper presents LGNet, a novel ultra-lightweight network specifically designed for edge deployment that achieves extreme model compression while maintaining detection performance through two core innovations. First, we develop a SAR-adapted Ghost-enhanced architecture that exploits inherent feature redundancy in SAR imagery through systematic integration of Ghost convolutions and hierarchical GHBlock modules, reducing redundant computation while preserving discriminative capabilities. Second, we introduce Layer-wise Adaptive Magnitude-based Pruning (LAMP) that assigns layer-specific sparsity levels based on multi-scale detection contributions, enabling intelligent compression with minimal accuracy loss. LGNet achieves remarkable efficiency gains: 75.3% parameter reduction and 59.3% FLOPs reduction compared to YOLOv8n baseline (from 3.0 M/8.1 G to 0.74 M/3.3 G) while delivering superior accuracy on SSDD (mAP@50: 97.9%, mAP@95: 71.9%) and strong generalization on RSDD-SAR (mAP@50: 94.4%). Extensive edge deployment validation demonstrates genuine real-time capability with 135.39 FPS performance on Huawei Atlas AIpro-20T edge computing platform, confirming practical viability for autonomous maritime systems and remote surveillance applications where computational resources are critically constrained. This work establishes that extreme model compression and high detection accuracy can coexist through principled SAR-specific lightweight design, enabling new paradigms for edge-based maritime monitoring networks.