Synthetic-to-Real Object Detection using YOLOv11 and Domain Randomization Strategies

• URL: http://arxiv.org/abs/2509.15045v1
• Date: Thu, 18 Sep 2025 15:08:54 GMT
• Title: Synthetic-to-Real Object Detection using YOLOv11 and Domain Randomization Strategies
• Abstract summary: This paper focuses on training a YOLOv11 model to detect a specific object (a soup can) using only synthetic data and domain randomization strategies.<n>The methodology involves extensive experimentation with data augmentation, dataset composition, and model scaling.<n>The best performing configuration, a YOLOv11l model trained on an expanded and diverse dataset, achieved a final mAP@50 of 0.910 on the competition's hidden test set.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper addresses the synthetic-to-real domain gap in object detection, focusing on training a YOLOv11 model to detect a specific object (a soup can) using only synthetic data and domain randomization strategies. The methodology involves extensive experimentation with data augmentation, dataset composition, and model scaling. While synthetic validation metrics were consistently high, they proved to be poor predictors of real-world performance. Consequently, models were also evaluated qualitatively, through visual inspection of predictions, and quantitatively, on a manually labeled real-world test set, to guide development. Final mAP@50 scores were provided by the official Kaggle competition. Key findings indicate that increasing synthetic dataset diversity, specifically by including varied perspectives and complex backgrounds, combined with carefully tuned data augmentation, were crucial in bridging the domain gap. The best performing configuration, a YOLOv11l model trained on an expanded and diverse dataset, achieved a final mAP@50 of 0.910 on the competition's hidden test set. This result demonstrates the potential of a synthetic-only training approach while also highlighting the remaining challenges in fully capturing real-world variability.

Related papers

• Sim-to-Real Fruit Detection Using Synthetic Data: Quantitative Evaluation and Embedded Deployment with Isaac Sim [0.0]
  This study investigates the effectiveness of synthetic data for sim-to-real transfer in object detection under constrained data conditions and embedded deployment requirements.<n>Results show that models trained exclusively on real data achieve the highest accuracy, while synthetic-only models exhibit reduced performance due to a domain gap.<n>The trained models were successfully deployed on a Jetson Orin NXRT, achieving real-time inference performance.
  arXiv  Detail & Related papers  (2026-03-30T16:52:29Z)
• Advancing Multinational License Plate Recognition Through Synthetic and Real Data Fusion: A Comprehensive Evaluation [3.3637719592955526]
  We explore the integration of real and synthetic data to enhance LPR performance.<n>Massive incorporation of synthetic data substantially boosts model performance in both intra- and cross-dataset scenarios.<n>Experiments underscore the efficacy of synthetic data in mitigating challenges posed by limited training data.
  arXiv  Detail & Related papers  (2026-01-12T15:52:52Z)
• Using Imperfect Synthetic Data in Downstream Inference Tasks [50.40949503799331]
  We introduce a new estimator based on generalized method of moments.<n>We find that interactions between the moment residuals of synthetic data and those of real data can improve estimates of the target parameter.
  arXiv  Detail & Related papers  (2025-08-08T18:32:52Z)
• Conditional Data Synthesis Augmentation [4.3108820946281945]
  Conditional Data Synthesis Augmentation (CoDSA) is a novel framework that synthesizes high-fidelity data for improving model performance across multimodal domains.<n>CoDSA fine-tunes pre-trained generative models to enhance the realism of synthetic data and increase sample density in sparse areas.<n>We introduce a theoretical framework that quantifies the statistical accuracy improvements enabled by CoDSA as a function of synthetic sample volume and targeted region allocation.
  arXiv  Detail & Related papers  (2025-04-10T03:38:11Z)
• Hybrid Training Approaches for LLMs: Leveraging Real and Synthetic Data to Enhance Model Performance in Domain-Specific Applications [0.0]
  This research explores a hybrid approach to fine-tuning large language models (LLMs) By leveraging a dataset combining transcribed real interactions with high-quality synthetic sessions, we aimed to overcome the limitations of domain-specific real data. The study evaluated three models: a base foundational model, a model fine-tuned with real data, and a hybrid fine-tuned model.
  arXiv  Detail & Related papers  (2024-10-11T18:16:03Z)
• Learning from Synthetic Data for Visual Grounding [55.21937116752679]
  We show that SynGround can improve the localization capabilities of off-the-shelf vision-and-language models.<n>Data generated with SynGround improves the pointing game accuracy of a pretrained ALBEF and BLIP models by 4.81% and 17.11% absolute percentage points, respectively.
  arXiv  Detail & Related papers  (2024-03-20T17:59:43Z)
• Consistency Regularization for Generalizable Source-free Domain Adaptation [62.654883736925456]
  Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. We propose a consistency regularization framework to develop a more generalizable SFDA method.
  arXiv  Detail & Related papers  (2023-08-03T07:45:53Z)
• Exploring the Effectiveness of Dataset Synthesis: An application of Apple Detection in Orchards [68.95806641664713]
  We explore the usability of Stable Diffusion 2.1-base for generating synthetic datasets of apple trees for object detection. We train a YOLOv5m object detection model to predict apples in a real-world apple detection dataset. Results demonstrate that the model trained on generated data is slightly underperforming compared to a baseline model trained on real-world images.
  arXiv  Detail & Related papers  (2023-06-20T09:46:01Z)
• Bridging the Gap: Enhancing the Utility of Synthetic Data via Post-Processing Techniques [7.967995669387532]
  generative models have emerged as a promising solution for generating synthetic datasets that can replace or augment real-world data. We propose three novel post-processing techniques to improve the quality and diversity of the synthetic dataset. Experiments show that Gap Filler (GaFi) effectively reduces the gap with real-accuracy scores to an error of 2.03%, 1.78%, and 3.99% on the Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets, respectively.
  arXiv  Detail & Related papers  (2023-05-17T10:50:38Z)
• One-Shot Domain Adaptive and Generalizable Semantic Segmentation with Class-Aware Cross-Domain Transformers [96.51828911883456]
  Unsupervised sim-to-real domain adaptation (UDA) for semantic segmentation aims to improve the real-world test performance of a model trained on simulated data. Traditional UDA often assumes that there are abundant unlabeled real-world data samples available during training for the adaptation. We explore the one-shot unsupervised sim-to-real domain adaptation (OSUDA) and generalization problem, where only one real-world data sample is available.
  arXiv  Detail & Related papers  (2022-12-14T15:54:15Z)
• Synthetic-to-Real Domain Generalized Semantic Segmentation for 3D Indoor Point Clouds [69.64240235315864]
  This paper introduces the synthetic-to-real domain generalization setting to this task. The domain gap between synthetic and real-world point cloud data mainly lies in the different layouts and point patterns. Experiments on the synthetic-to-real benchmark demonstrate that both CINMix and multi-prototypes can narrow the distribution gap.
  arXiv  Detail & Related papers  (2022-12-09T05:07:43Z)
• CAFE: Learning to Condense Dataset by Aligning Features [72.99394941348757]
  We propose a novel scheme to Condense dataset by Aligning FEatures (CAFE) At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales. We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art.
  arXiv  Detail & Related papers  (2022-03-03T05:58:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.