Novel Methodology for Vision Backbone Network Fine-Tuning and Continual Learning in Optical Inspection Tasks

Roll-to-roll (R2R) manufacturing plays a key role in the production of electronic devices, liquid-crystal films, and other advanced materials. In these processes, maintaining precise material thickness and surface uniformity is essential to ensure product quality, process efficiency, and yield. While optical inspection systems are widely used to detect surface defects, using AI to automatically segment and classify the defect remains a challenging task. This paper investigates a new methodology to establish an AI powered optical inspection testbed that automatically handles multi-label defect classification and semantic segmentation in R2R manufacturing environments. The proposed system integrates semantic segmentation and multi-label defect classification into an automated optical inspection pipeline. Using a Vision Transformer deep learning architecture, our classification model achieved a high accuracy across multiple surface defect categories in a production-representative dataset. Our framework consists of automatic image pre-processing, rapid finetuning of pre-train vision models, and a continuous learning strategy after the model is deployed. The system is intended to handle high-throughput image data with minimal latency, enabling fast and accurate product inspection while maintaining a light-weight model architecture. The end-to-end framework is designed to enhance in-line defect detection, reduce human dependency, and improve overall process visibility. The proposed technology has been successfully deployed to a manufacturing plan and has achieved satisfactory detection and classification performance in a real operation environment.