Intelligent recognition of GPR road hidden defect images based on feature fusion and attention mechanism

TL;DR

This paper presents a novel deep learning framework combining feature fusion and attention mechanisms for accurate, automated detection of subsurface road defects in GPR images, improving robustness and efficiency.

Contribution

It introduces a new MCGA-Net model with feature fusion and attention, along with data augmentation via DCGAN, advancing GPR defect detection accuracy and automation.

Findings

Achieves 92.8% precision and 95.9% mAP@50.

Maintains robustness against noise and small targets.

Outperforms existing models in complex environments.

Abstract

Ground Penetrating Radar (GPR) has emerged as a pivotal tool for non-destructive evaluation of subsurface road defects. However, conventional GPR image interpretation remains heavily reliant on subjective expertise, introducing inefficiencies and inaccuracies. This study introduces a comprehensive framework to address these limitations: (1) A DCGAN-based data augmentation strategy synthesizes high-fidelity GPR images to mitigate data scarcity while preserving defect morphology under complex backgrounds; (2) A novel Multi-modal Chain and Global Attention Network (MCGA-Net) is proposed, integrating Multi-modal Chain Feature Fusion (MCFF) for hierarchical multi-scale defect representation and Global Attention Mechanism (GAM) for context-aware feature enhancement; (3) MS COCO transfer learning fine-tunes the backbone network, accelerating convergence and improving generalization. Ablation and comparison experiments validate the framework's efficacy. MCGA-Net achieves Precision (92.8%), Recall (92.5%), and mAP@50 (95.9%). In the detection of Gaussian noise, weak signals and small targets, MCGA-Net maintains robustness and outperforms other models. This work establishes a new paradigm for automated GPR-based defect detection, balancing computational efficiency with high accuracy in complex subsurface environments.