ADRL-DETR: Adaptive Dual-Path Representation Learning With Receptive Field Modulation for Tunnel Crack Detection

Real-time detection of tunnel-lining cracks is hindered by low contrast, irregular morphology, and complex background textures. We propose ADRL-DETR (Adaptive Dual-Path Representation Learning DETR), a lightweight transformer-based detector that introduces a spatial–scale dual-path adaptive mechanism to enhance spatial feature discrimination and enable cross-scale fusion guided by dynamic receptive-field modulation. The Global–Local Adaptive Filtering Block (GLAFB) serves as the multi-spatial feature expression path, adaptively separating cracks from background via complementary global–local filtering, while the Scale-Adaptive Fusion Block (SAFB) functions as the cross-scale fusion path, integrating multi-dilated convolutions and channel attention to achieve dynamic receptive-field adjustment and synergistic feature optimization. On the MixCrack-6K dataset, ADRL-DETR attains 87.8% Precision and 78.8% AP50with 51.0 GFLOPs, outperforming the baseline by 3.2% Precision and 3.1% AP50. The proposed framework achieves an effective trade-off between accuracy and efficiency, offering an interpretable and deployable solution for real-time tunnel inspection.