Detecting pedestrians in crowded scenes is a challenging task due to severe occlusions and complex object interactions. To improve data augmentation for this setting, we propose a novel Scoring-based Copy-Paste Pipeline (SCPP). Unlike previous methods that randomly paste objects near each other, SCPP introduces a principled three-stage augmentation pipeline that incorporates patch scoring and flexible object placement. We design a new metric called Neighbor-Weighted IoU ( \(IoU_{\text {nw}}\) ) to more effectively capture the complexity of object overlaps. Our approach ensures more diverse occlusion patterns, as well as better crowdedness consistency across pasted patches. We conduct experiments on the CrowdHuman dataset using mainstream detectors, showing that SCPP consistently outperforms existing targeted copy-paste techniques on standard evaluation metrics. Notably, these improvements are achieved without additional training data or complex post-processing, demonstrating the potential of our approach for enhancing pedestrian detection performance in crowded scenarios.