Adapting OpenAI's CLIP Model for Few-Shot Image Inspection in Manufacturing Quality Control: An Expository Case Study with Multiple Application Examples
Journal:
arXiv
Published Date:
Jan 22, 2025
Abstract
This expository paper introduces a simplified approach to image-based quality
inspection in manufacturing using OpenAI's CLIP (Contrastive Language-Image
Pretraining) model adapted for few-shot learning. While CLIP has demonstrated
impressive capabilities in general computer vision tasks, its direct
application to manufacturing inspection presents challenges due to the domain
gap between its training data and industrial applications. We evaluate CLIP's
effectiveness through five case studies: metallic pan surface inspection, 3D
printing extrusion profile analysis, stochastic textured surface evaluation,
automotive assembly inspection, and microstructure image classification. Our
results show that CLIP can achieve high classification accuracy with relatively
small learning sets (50-100 examples per class) for single-component and
texture-based applications. However, the performance degrades with complex
multi-component scenes. We provide a practical implementation framework that
enables quality engineers to quickly assess CLIP's suitability for their
specific applications before pursuing more complex solutions. This work
establishes CLIP-based few-shot learning as an effective baseline approach that
balances implementation simplicity with robust performance, demonstrated in
several manufacturing quality control applications.
