Fully-Synthetic Training for Visual Quality Inspection in Automotive Production
Journal:
arXiv
Published Date:
Mar 12, 2025
Abstract
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.
