# SYNOSIS: Image Synthesis Pipeline for Machine Vision in Metal Surface Inspection

## TL;DR

This paper introduces a new pipeline for generating realistic synthetic images of metal surfaces to improve machine vision inspection systems.

## Contribution

The novel contribution is a synthetic data generation pipeline with precise control over texture parameters for metal surface inspection.

## Key findings

- The pipeline generates physically realistic textures with interpretable parameters.
- The synthetic dataset includes sandblasting, parallel, and spiral milling textures.
- Image similarity metrics between real and synthetic data correlate with downstream detection performance.

## Abstract

The use of machine learning methods for the development of robust and flexible visual inspection systems has shown promising results. However, their performance is highly dependent on the large amount and diversity of training data, which is difficult to obtain in practice. Recent developments in synthetic dataset generation have seen increasing success in overcoming these problems. However, the prevailing work revolves around the usage of generative models, which suffer from data shortages, hallucinations, and provide limited support for unobserved edge-cases. In this work, we present the first synthetic data generation pipeline that is capable of generating large datasets of physically realistic textures exhibiting sophisticated structured patterns. Our framework is based on procedural texture modelling with interpretable parameters, uniquely allowing us to guarantee precise control over the texture parameters as we generate a high variety of observed and unobserved texture instances. We publish the dual dataset used in this paper, presenting models of sandblasting, parallel, and spiral milling textures, which are commonly present on manufactured metal products. To evaluate the dataset quality, we go beyond final model performance comparison by measuring different image similarities between the real and synthetic domains. This uncovered a trend, indicating these metrics could be used to predict downstream detection performance, which can strongly impact future developments of synthetic data.