Using In-Context Learning for Automatic Defect Labelling of Display Manufacturing Data

2025-06-05

AI Summary

To address the high cost and low efficiency of manual defect annotation in display panel manufacturing, this paper proposes an AI-assisted automatic annotation system. Methodologically, we are the first to adapt SegGPT to industrial defect detection, introducing a domain-adaptive two-stage training paradigm—domain-specific pretraining followed by defect-aware fine-tuning—and incorporating a lightweight scribble-based annotation mechanism with a scribble-to-mask supervision strategy. Our key contributions are: (1) effective adaptation to industrial small-sample, multi-model production line data; and (2) substantial reduction in annotation dependency. Experiments on multi-model production line datasets demonstrate an average IoU improvement of 0.22, a 14% increase in recall, and an automatic annotation coverage rate of 60%. Critically, the model trained with our method achieves performance comparable to that of models trained exclusively on fully manual annotations.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Agentic search

Abstract

This paper presents an AI-assisted auto-labeling system for display panel defect detection that leverages in-context learning capabilities. We adopt and enhance the SegGPT architecture with several domain-specific training techniques and introduce a scribble-based annotation mechanism to streamline the labeling process. Our two-stage training approach, validated on industrial display panel datasets, demonstrates significant improvements over the baseline model, achieving an average IoU increase of 0.22 and a 14% improvement in recall across multiple product types, while maintaining approximately 60% auto-labeling coverage. Experimental results show that models trained on our auto-labeled data match the performance of those trained on human-labeled data, offering a practical solution for reducing manual annotation efforts in industrial inspection systems.

Problem

• Automating defect labeling in display manufacturing using AI
• Improving defect detection accuracy with SegGPT enhancements
• Reducing manual annotation in industrial inspection systems

Innovation

• In-context learning for defect labeling
• Enhanced SegGPT with domain-specific techniques
• Scribble-based annotation mechanism introduced