To address issues such as low detection accuracy, high model complexity, and insufficient attention to defect boundary information in the steering knuckle surface defect detection process, this paper proposes an improved RT-DETR-based steering knuckle surface defect detection algorithm GSG-DETR. First, a multi-scale edge information transfer module GLOFT is designed to improve the backbone network by enhancing the capture and transfer of edge information, thus increasing the model′s sensitivity to defect boundaries. Next, a selective edge information aggregation module SBA is introduced into the neck network, constructing an adaptive fusion mechanism between low-resolution boundary information and deep semantic features, optimizing the alignment strategy of multi-scale defect boundary features. Finally, a GroupNorm-based structured pruning method is employed to eliminate redundant coupled layers, reducing the model′s parameter count and computational complexity. Experimental results demonstrate that the GSG-DETR algorithm achieves an mAP50 of 88.2% in the steering knuckle crack detection task, a 2.0% improvement over the baseline model, with a 34.3% reduction in parameters and a 32.1% reduction in computational complexity, while the FPS increases to 105.1 frames. Further validation on the NEU-DET dataset shows that the improved algorithm yields a 4.3% increase in mAP50 compared to the baseline model. In summary, GSG-DETR not only excels in detection accuracy but also aligns better with practical applications.