This paper presents LP-DETR (Layer-wise Progressive Relation DETR), a novel approach that enhances DETR-based object detection through multi-scale relation modeling. Our method introduces learnable spatial relationships between object queries through a relation-aware self-attention mechanism, which adaptively learns to balance different scales of relations (local, medium, and global) across decoder layers. This progressive design enables the model to effectively capture evolving spatial dependencies throughout the detection pipeline. Extensive experiments on the COCO 2017 dataset demonstrate that our method improves both convergence speed and detection accuracy compared to the standard self-attention module. The proposed method achieves competitive results, reaching 52.3% AP with 12 epochs and 52.5% AP with 24 epochs using the ResNet-50 backbone, and further improving to 58.0% AP with the Swin-L backbone. Furthermore, our analysis reveals a learning pattern: The model naturally learns to prioritize local spatial relations in early decoder layers, while gradually shifting attention to broader contexts in deeper layers, providing valuable insights about spatial relations for future research in object detection.