合成数据能有效提升仓储物体检测模型性能。
The Impact of Synthetic Data on Object Detection Model Performance: A Comparative Analysis with Real-World Data
- 用NVIDIA Omniverse Replicator生成合成数据，辅助训练检测模型。
- 混合使用合成与真实数据的模型在真实场景表现更优。
- 适合资源有限但需高效部署视觉系统的工业场景。
生成式AI在计算机视觉领域的进展为物流和制造等行业优化工作流程提供了新机遇。然而，许多AI应用受限于专业人才与资源，往往依赖通用模型，而这些模型的成功通常需要领域特定数据进行微调，这既昂贵又低效。因此，使用合成数据进行微调成为一种成本效益高的替代方案。本文研究了合成数据对物体检测模型性能的影响，对比了仅使用真实数据训练的模型。实验聚焦于仓库环境中托盘检测任务，采用真实数据与多种合成数据生成策略。结果表明，合理融合合成与真实数据可构建出鲁棒且高效的检测模型，为计算机视觉中合成图像数据的实际应用提供重要参考。
原文摘要 · Abstract (English)
Recent advances in generative AI, particularly in computer vision (CV), offer new opportunities to optimize workflows across industries, including logistics and manufacturing. However, many AI applications are limited by a lack of expertise and resources, which forces a reliance on general-purpose models. Success with these models often requires domain-specific data for fine-tuning, which can be costly and inefficient. Thus, using synthetic data for fine-tuning is a popular, cost-effective alternative to gathering real-world data. This work investigates the impact of synthetic data on the performance of object detection models, compared to models trained on real-world data only, specifically within the domain of warehouse logistics. To this end, we examined the impact of synthetic data generated using the NVIDIA Omniverse Replicator tool on the effectiveness of object detection models in real-world scenarios. It comprises experiments focused on pallet detection in a warehouse setting, utilizing both real and various synthetic dataset generation strategies. Our findings provide valuable insights into the practical applications of synthetic image data in computer vision, suggesting that a balanced integration of synthetic and real data can lead to robust and efficient object detection models.
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