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

Problem

Data collection for object detection in critical domains is costly and constrained. This paper addresses the gap in literature regarding the balance of datasets derived from real, simulated, and generative sources, particularly in the context of enhancing robustness in object detection models. The work is presented as a preprint and has not undergone peer review.

Method

The authors propose a unified experimental framework that integrates various data generation paradigms, specifically Unity Simulation-based rendering and Controllable Diffusion-based generation (CIA). The evaluation metrics employed include Precision, Recall, mean Average Precision (mAP), and custom $Δ$-metrics to quantify dataset balance and model performance. The training configuration ensures controlled dataset mixing across real, simulated, and generative sources, maintaining identical model architectures and training settings throughout the experiments.

Results

The results indicate significant performance variations based on the dataset composition:

• mAP@0.5 (Unity-only training): A $-50%$ drop in performance compared to training on real data.
• mAP@0.5 (CIA-only training): A degradation of $-16.5\%$ relative to real data.
• mAP@0.5 (90% real + 10% Unity): Achieved a score of $62.68\%$, which is a $+7.64\%$ improvement over the baseline.
• Precision (90% real + 10% CIA): Reported at $74.45\%$, with no baseline provided for comparison. The available text does not report quantitative results for Recall or other metrics.

Limitations

The authors note that excessive substitution of synthetic data can lead to domain drift, which may adversely affect model performance. This limitation highlights the potential risks associated with over-reliance on synthetic data in training regimes. Additionally, the paper does not address the long-term implications of dataset balance on model generalization across diverse real-world scenarios.

Why it matters

This research has significant implications for downstream work in object detection, particularly in critical applications where data scarcity is a challenge. By quantifying the effects of dataset balance and exploring innovative data generation techniques, the findings can inform best practices for dataset construction and model training, ultimately leading to more robust and reliable object detection systems.