Synthetic Similarity Search in Automotive Production
This addresses the costly and time-consuming data collection for computer vision models in automotive production, though it is incremental as it builds on existing similarity search and synthetic data methods.
The paper tackles the problem of reducing the need for large annotated datasets for visual quality inspection in automotive production by proposing a pipeline that combines similarity search using a vision-based foundation model with synthetic data, achieving high classification accuracy in eight real-world scenarios.
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.