Mohamed Benkedadra

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

Quantitative evaluation using Precision, Recall, mAP, and custom Δ -metrics, reveals that neither simulation nor generative augmentation alone achieves optimal transferability, and results demonstrate that limited synthetic inclusion enhances generalization, while excessive substitution induces domain drift.