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The Impact of Synthetic Data on Object Detection Model Performance: A Comparative Analysis with Real-World Data

Muammer Bay, Timo von Marcard, Dren Fazlija

TL;DR

The paper investigates whether synthetic data can substitute real-world data for fine-tuning object detectors in warehouse logistics, focusing on pallet detection. Using three synthetic realism levels (Realistic, Half-Realistic, Random) and two fine-tuning strategies (Mixed training and Bridged Transfer Learning), the study evaluates performance on in-distribution (ID) and out-of-distribution (OOD) test sets, quantified by $AP@0.5:0.95$. Key findings show that synthetic data alone is not sufficient, but combining synthetic with limited real data yields gains, with Bridged Transfer Learning frequently outperforming mixing, especially in low-data regimes; Half-Realistic improves ID while Random enhances OOD robustness. The work provides actionable guidance for SMEs evaluating synthetic-data pipelines and contributes reproducible benchmarks through open data and code.

Abstract

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.

The Impact of Synthetic Data on Object Detection Model Performance: A Comparative Analysis with Real-World Data

TL;DR

The paper investigates whether synthetic data can substitute real-world data for fine-tuning object detectors in warehouse logistics, focusing on pallet detection. Using three synthetic realism levels (Realistic, Half-Realistic, Random) and two fine-tuning strategies (Mixed training and Bridged Transfer Learning), the study evaluates performance on in-distribution (ID) and out-of-distribution (OOD) test sets, quantified by . Key findings show that synthetic data alone is not sufficient, but combining synthetic with limited real data yields gains, with Bridged Transfer Learning frequently outperforming mixing, especially in low-data regimes; Half-Realistic improves ID while Random enhances OOD robustness. The work provides actionable guidance for SMEs evaluating synthetic-data pipelines and contributes reproducible benchmarks through open data and code.

Abstract

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.
Paper Structure (24 sections, 2 equations, 12 figures, 2 tables)

This paper contains 24 sections, 2 equations, 12 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Contributions.
  3. Reproducibility.
  4. Related Work
  5. Synthetic Data Generation via 3D Engines
  6. Synthetic vs. Real Data
  7. Dataset Creation Process
  8. Real-World Data Acquisition
  9. Synthetic Data Generation
  10. Data Processing
  11. Experiments
  12. The Object Detection Model
  13. Experimental Setup
  14. Results
  15. Synthetic Data Only
  16. ...and 9 more sections

Figures (12)

  • Figure 1: Overview of our Four-Stage Experimental Workflow. The process begins with acquiring real-world and synthetic data, followed by data preparation and processing. In the model setup phase, models are configured and trained. Afterwards, we assess the fine-tuned models against various benchmarks.
  • Figure 2: A comparison between an in-distribution sample (left) and an out-of-distribution image (right). The latter image displays a pallet in an unusual color and orientation.
  • Figure 3: A comparison between a realistic synthetic image (left) and a real-world image (right).
  • Figure 4: Examples of Half-Realistic Synthetic Images.
  • Figure 5: Examples of Random Synthetic Images.
  • ...and 7 more figures