To address the issues of missed detection of tiny defects, background interference, and insufficient real-time performance in surface defect detection of metal rods, an improved RT-DETR-based efficient detection algorithm, RDGS-DETR, is proposed. A lightweight feature extraction module, called the reparameterized-partial feature network block (RPFN block), is designed. It integrates structural re-parameterization with sparse channel computation to reduce parameter complexity while enhancing the representation of micro-crack features. A dynamic feature refinement fusion module (DFRFM) is also developed, incorporating the DySample dynamic upsampling operator, which improves multi-scale feature alignment accuracy in curved surface imaging scenarios by adaptively predicting offsets. Furthermore, a geometric-sensitive normalized loss (GSNL) is introduced to address the limited sensitivity of traditional IoU metrics to non-overlapping small targets and to reduce regression bias for complex-shaped defects. In addition, a sparse global interaction attention (SGIA) module is developed, which employs an efficient additive attention mechanism to achieve global context modeling of defect regions with linear complexity. Experimental results demonstrate that, compared with the original model, RDGS-DETR improves inference speed by 8.55 FPS and increases mAP@0.5 by 2.8%, while also verifying its robustness. The algorithm achieves a balance between detection accuracy and real-time performance, providing reliable technical support for surface quality inspection of metal rods in intelligent manufacturing scenarios.