To address the challenges of limited fabric defect categories, significant scale variations, and low detection accuracy in existing models, this study introduces DHR-DETR, a fabric defect detection method based on RT-DETR. Firstly, a Multi-Path Coordinate Attention(MPCA) module is innovatively designed and deeply integrated with the Deformable Convolution Module(DCNv2) to construct a Dynamic Deformable Convolution Module. This integration effectively accommodates the diverse and complex shapes of fabric defects. Secondly, a High-Level Screening Feature Pyramid Network(HS-FPN) is employed to replace the Cross-Scale Feature Fusion Module(CCFM), enabling efficient multi-level feature fusion while significantly reducing model complexity. Finally, a lightweight yet feature-enhancing RetBlockC3 module is developed and incorporated into the HS-FPN network. This module enhances the model′s capability to capture local information and further improves its lightweight design.Experimental evaluations demonstrate that the proposed DHR-DETR method achieves mAP@0.5 scores of 50.9% and 97.5% on public and custom fabric datasets, respectively, reflecting improvements of 2.9% and 0.6% compared to the baseline model. Additionally, the parameter count is reduced to just 17.9 M, with a 37% decrease in computational complexity. These results indicate substantial improvements in detection performance and deployment efficiency, showcasing the potential of DHR-DETR for practical applications in industrial fabric inspection tasks.