# SYNOSIS: Image synthesis pipeline for machine vision in metal surface inspection

arXiv: https://arxiv.org/abs/2410.14844 · HTML: https://mlsanity.com/paper/2410.14844/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 127 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Builds on (most cited first, 30 of 34)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Kubric: A scalable dataset generator](https://mlsanity.com/paper/2203.03570/) (2022)
- [Learning from Synthetic Humans](https://mlsanity.com/paper/1701.01370/) (2017)
- [Virtual Worlds as Proxy for Multi-Object Tracking Analysis](https://mlsanity.com/paper/1605.06457/) (2016)
- [Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization](https://mlsanity.com/paper/1804.06516/) (2018)
- [A Brief Review of Domain Adaptation](https://mlsanity.com/paper/2010.03978/) (2020)
- [Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects](https://mlsanity.com/paper/1809.10790/) (2018)
- [VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization](https://mlsanity.com/paper/2104.10036/) (2021)
- [Fake It Till You Make It: Face analysis in the wild using synthetic data alone](https://mlsanity.com/paper/2109.15102/) (2021)
- [Segmentation-Based Deep-Learning Approach for Surface-Defect Detection](https://mlsanity.com/paper/1903.08536/) (2019)
- [Mixed supervision for surface-defect detection: from weakly to fully supervised learning](https://mlsanity.com/paper/2104.06064/) (2021)
- [Learning to Detect and Track Visible and Occluded Body Joints in a Virtual World](https://mlsanity.com/paper/1803.08319/) (2018)
- [Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection](https://mlsanity.com/paper/2103.15158/) (2021)
- [Learning Texture Manifolds with the Periodic Spatial GAN](https://mlsanity.com/paper/1705.06566/) (2017)
- [Texture Synthesis with Spatial Generative Adversarial Networks](https://mlsanity.com/paper/1611.08207/) (2016)
- [Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception](https://mlsanity.com/paper/1905.00160/) (2019)
- [An Inverse Procedural Modeling Pipeline for SVBRDF Maps](https://mlsanity.com/paper/2109.06395/) (2021)
- [Training Deep Face Recognition Systems with Synthetic Data](https://mlsanity.com/paper/1802.05891/) (2018)
- [Real-Time Neural Appearance Models](https://mlsanity.com/paper/2305.02678/) (2023)
- [MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2211.13968/) (2022)
- [Play and Learn: Using Video Games to Train Computer Vision Models](https://mlsanity.com/paper/1608.01745/) (2016)
- [A survey of exemplar-based texture synthesis](https://mlsanity.com/paper/1707.07184/) (2017)
- [CAD2Render: A Modular Toolkit for GPU-accelerated Photorealistic Synthetic Data Generation for the Manufacturing Industry](https://mlsanity.com/paper/2211.14054/) (2022)
- [Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation](https://mlsanity.com/paper/2302.08366/) (2023)
- [Dataset of Industrial Metal Objects](https://mlsanity.com/paper/2208.04052/) (2022)
- [Towards Sim-to-Real Industrial Parts Classification with Synthetic Dataset](https://mlsanity.com/paper/2404.08778/) (2024)

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

- [SynthRender and I-AsSET: Open-Source Framework and Dataset for Bidirectional Sim-Real Transfer in Industrial Object Perception](https://mlsanity.com/paper/2602.21141/) (2026)
- [Synthetic Defect Geometries of Cast Metal Objects Modeled via 2d Voronoi Tessellations](https://mlsanity.com/paper/2602.05440/) (2026)

## Abstract (arXiv)

The use of machine learning (ML) methods for development of robust and flexible visual inspection system has shown promising. However their performance is highly dependent on the amount and diversity of training data. This is often restricted not only due to costs but also due to a wide variety of defects and product surfaces which occur with varying frequency. As such, one can not guarantee that the acquired dataset contains enough defect and product surface occurrences which are needed to develop a robust model. Using parametric synthetic dataset generation, it is possible to avoid these issues. In this work, we introduce a complete pipeline which describes in detail how to approach image synthesis for surface inspection - from first acquisition, to texture and defect modeling, data generation, comparison to real data and finally use of the synthetic data to train a defect segmentation model. The pipeline is in detail evaluated for milled and sandblasted aluminum surfaces. In addition to providing an in-depth view into each step, discussion of chosen methods, and presentation of ML results, we provide a comprehensive dual dataset containing both real and synthetic images.