Transformer-based data generation and lightweight robust detection network for complex pavement defects

Abstract

Ground-penetrating radar (GPR) accurate detection of road surface hidden defects is very important for road maintenance. However, due to the complexity of disease waveforms and the scarcity of sample data, existing models face problems such as insufficient accuracy and have weak generalization ability in engineering scenarios. To this end, this paper proposes a two-stage method for improving the accuracy of complex pavement diseases with small-scale data. Firstly, a B-scan Latent Generative Adversarial Network (BL-GAN) was proposed to effectively expand the small-scale dataset by combining the self-attention mechanism and hierarchical feature fusion to synthesize realistic defect patterns. The t-SNE visualization confirms the high feature agreement between the synthetic and real defect images. Secondly, by systematically integrating MobileViTv2 and the channel attention mechanism, a lightweight transformer-based detector ECA-MobileViTv2 YOLOv5s(EV2-YOLOv5s) is designed to achieve accurate localization of multi-scale defects while maintaining computational efficiency. Experimental results show that the proposed method achieves 96.5% [email protected] with 144.1 FPS (GPU) inference speed, 19.7 FPS (CPU) inference speed, and 7.4 MB model size, which is significantly better than traditional methods. Finally, the contributions of Multi-head Attention (MHA) and Linearly Separable Self-Attention (LSA) in global feature aggregation and local feature extraction were analyzed to support the subsequent optimization of Transformer-based defect detection algorithms. This work provides a practical solution for the detection of hidden road surface diseases in resource-constrained scenarios.