Expanding Domain Coverage in Injection Molding Quality Inspection with Physically-Based Synthetic Data

Dominik Schraml, Dominik Schraml, Gunther Notni

2025

Abstract

Synthetic data has emerged as a vital tool in computer vision research, yet procedural generation using 3D computer graphics remains underexplored compared to generative adversarial networks (GANs). Our method offers greater control over generated images, making it particularly valuable for domains like industrial quality inspection, where real data is often sparse. We present a method for generating physically based rendered images of an injection-molded cup, simulating two common defects - short shot and color streak. The approach automates defect generation with variable size and severity, along with pixel-perfect segmentation masks, significantly reducing labeling effort. Synthetic data was combined with a small set of real images to train semantic segmentation models and explore domain expansion, such as inspecting parts in novel colors not represented in real-world datasets. Experiments demonstrate that the method enhances defect detection and is especially effective for domain expansion tasks, such as inspecting parts in new colors. However, challenges persist in segmenting smaller defects, underscoring the need for balanced synthetic datasets and probably also for customized loss functions.

Paper Citation

in Harvard Style

Schraml D. and Notni G. (2025). Expanding Domain Coverage in Injection Molding Quality Inspection with Physically-Based Synthetic Data. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 503-510. DOI: 10.5220/0013252000003912

in Bibtex Style

@conference{visapp25,
author={Dominik Schraml and Gunther Notni},
title={Expanding Domain Coverage in Injection Molding Quality Inspection with Physically-Based Synthetic Data},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={503-510},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013252000003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Expanding Domain Coverage in Injection Molding Quality Inspection with Physically-Based Synthetic Data
SN - 978-989-758-728-3
AU - Schraml D.
AU - Notni G.
PY - 2025
SP - 503
EP - 510
DO - 10.5220/0013252000003912
PB - SciTePress