From Unity Simulation to Diffusion-Based Augmentation: Quantifying Dataset Balance for Robust Object Detection

Modern computer vision models achieve high accuracy when trained on large-scale annotated datasets. In critical domains such as construction safety monitoring, data collection is costly, hazardous, and ethically constrained. This paper presents a systematic study comparing two complementary data generation paradigms, (1) Unity Simulation-based rendering and (2) Controllable Diffusion-based generation (CIA), for object detection under real data-scarce conditions. A unified experimental framework enables controlled dataset mixing across real, simulated, and generative sources, while maintaining identical model and training settings. Quantitative evaluation using Precision, Recall, mAP, and custom Delta-metrics, reveals that neither simulation nor generative augmentation alone achieves optimal transferability. Unity-only training yields an [email protected] drop of -50% relative to real data, while CIA-only training shows a milder -16.5% degradation. Hybrid compositions significantly improve performance, with the 90% real + 10% Unity configuration achieving the best overall [email protected] of 62.68% (+7.64% over baseline), and the 90% real + 10% CIA configuration maximizing precision at 74.45%. Results demonstrate that limited synthetic inclusion enhances generalization, while excessive substitution induces domain drift.