# Fully-Synthetic Training for Visual Quality Inspection in Automotive Production

arXiv: https://arxiv.org/abs/2503.09354 · HTML: https://mlsanity.com/paper/2503.09354/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 12 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1607.03516/) (2016)
- [Object Detection Using Sim2Real Domain Randomization for Robotic Applications](https://mlsanity.com/paper/2208.04171/) (2022)
- [RenderGAN: Generating Realistic Labeled Data](https://mlsanity.com/paper/1611.01331/) (2016)
- [CAD2Render: A Modular Toolkit for GPU-accelerated Photorealistic Synthetic Data Generation for the Manufacturing Industry](https://mlsanity.com/paper/2211.14054/) (2022)
- [Towards Sim-to-Real Industrial Parts Classification with Synthetic Dataset](https://mlsanity.com/paper/2404.08778/) (2024)
- [Synthetic Data Augmentation Using GAN For Improved Automated Visual Inspection](https://mlsanity.com/paper/2212.09317/) (2022)

## Built on by (most cited first, 2 of 2)

- [Synthetic Similarity Search in Automotive Production](https://mlsanity.com/paper/2505.07256/) (2025)
- [Contrastive Augmented Transformer with Domain-specific Enhancement for Robust Multi-scenario Metal Surface Defect Detection](https://mlsanity.com/paper/2606.01962/) (2026)

## Abstract (arXiv)

Visual Quality Inspection plays a crucial role in modern manufacturing environments as it ensures customer safety and satisfaction. The introduction of Computer Vision (CV) has revolutionized visual quality inspection by improving the accuracy and efficiency of defect detection. However, traditional CV models heavily rely on extensive datasets for training, which can be costly, time-consuming, and error-prone. To overcome these challenges, synthetic images have emerged as a promising alternative. They offer a cost-effective solution with automatically generated labels. In this paper, we propose a pipeline for generating synthetic images using domain randomization. We evaluate our approach in three real inspection scenarios and demonstrate that an object detection model trained solely on synthetic data can outperform models trained on real images.