# Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images

arXiv: https://arxiv.org/abs/1805.11778 · HTML: https://mlsanity.com/paper/1805.11778/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2018.

## Built on by (most cited first, 4 of 4)

- [Grapevine Winter Pruning Automation: On Potential Pruning Points Detection through 2D Plant Modeling using Grapevine Segmentation](https://mlsanity.com/paper/2106.04208/) (2021)
- [Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis](https://mlsanity.com/paper/2303.10202/) (2023)
- [Towards Precise Pruning Points Detection using Semantic-Instance-Aware Plant Models for Grapevine Winter Pruning Automation](https://mlsanity.com/paper/2109.07247/) (2021)
- [The Power of Light: Improving Synthetic-to-Real Domain Adaptation through Physically-Based Indirect Illumination](https://mlsanity.com/paper/2606.22574/) (2026)

## Abstract (arXiv)

In this work, we present an application of domain randomization and generative adversarial networks (GAN) to train a near real-time object detector for industrial electric parts, entirely in a simulated environment. Large scale availability of labelled real world data is typically rare and difficult to obtain in many industrial settings. As such here, only a few hundred of unlabelled real images are used to train a Cyclic-GAN network, in combination with various degree of domain randomization procedures. We demonstrate that this enables robust translation of synthetic images to the real world domain. We show that a combination of the original synthetic (simulation) and GAN translated images, when used for training a Mask-RCNN object detection network achieves greater than 0.95 mean average precision in detecting and classifying a collection of industrial electric parts. We evaluate the performance across different combinations of training data.