LGNet: A Lightweight Ghost-Enhanced Network for Efficient SAR Ship Detection

Abstract

Highlights: What are the main findings? A novel SAR-adapted Ghost-enhanced architecture (GHBlock + HGStem) systematically exploits inherent feature redundancy in SAR imagery, effectively addressing domain-specific challenges including speckle noise suppression and sea clutter discrimination. The Layer-wise Adaptive Magnitude-based Pruning (LAMP) strategy intelligently assigns layer-specific sparsity levels based on multi-scale detection contributions, achieving substantial compression (75.3% parameter reduction and 59.3% FLOPs reduction) while preserving superior detection performance. What is the implication of the main finding? This work demonstrates that SAR-specific lightweight architectures can simultaneously achieve extreme model compression and maintain high detection accuracy, thereby enabling practical deployment on resource-constrained edge computing platforms (135.39 FPS on hardware-constrained devices). The proposed dual lightweight methodology establishes a comprehensive framework for edge-based maritime surveillance systems, providing theoretical and practical foundations for real-time SAR ship detection in distributed monitoring networks. 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.