用上下文学习自动标注显示面板缺陷，提升标注效率与模型性能。
Using In-Context Learning for Automatic Defect Labelling of Display Manufacturing Data
- 基于草图标注和领域优化的SegGPT架构，实现高效缺陷标注。
- 平均交并比提升0.22，召回率提高14%，自动生成标签覆盖率达60%。
- 自标注数据训练模型媲美人工标注，适合工业质检场景应用。
本文提出一种基于上下文学习的AI辅助自动标注系统，用于显示面板缺陷检测。通过改进SegGPT架构并引入领域特定训练方法及草图式标注机制，构建两阶段训练流程。在工业显示面板数据集上的验证表明，该方法相较基线模型平均交并比提升0.22，跨多种产品类型召回率提高14%，同时保持约60%的自动标注覆盖率。实验结果表明，使用自标注数据训练的模型性能可媲美人工标注数据训练的模型，为工业检测系统中降低人工标注成本提供了实用解决方案。
原文摘要 · Abstract (English)
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.
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