Synthetic Similarity Search in Automotive Production
Journal:
arXiv
Published Date:
May 12, 2025
Abstract
Visual quality inspection in automotive production is essential for ensuring
the safety and reliability of vehicles. Computer vision (CV) has become a
popular solution for these inspections due to its cost-effectiveness and
reliability. However, CV models require large, annotated datasets, which are
costly and time-consuming to collect. To reduce the need for extensive training
data, we propose a novel image classification pipeline that combines similarity
search using a vision-based foundation model with synthetic data. Our approach
leverages a DINOv2 model to transform input images into feature vectors, which
are then compared to pre-classified reference images using cosine distance
measurements. By utilizing synthetic data instead of real images as references,
our pipeline achieves high classification accuracy without relying on real
data. We evaluate this approach in eight real-world inspection scenarios and
demonstrate that it meets the high performance requirements of production
environments.
