用生成模型扩充缺陷数据，提升工业质检的图像分割准确率
Improving Supervised Machine Learning Performance in Optical Quality Control via Generative AI for Dataset Expansion
- 用Stable Diffusion生成热成像中的缺陷样本，扩展稀缺数据集
- 使图像分割的平均交并比提升至84.6%，改善4.6个百分点
- 适合关注工业视觉质检与生成式数据增强的研究者
监督学习在工业生产中的光学质量控制中至关重要，但实际生产中缺陷部件稀少，导致数据严重不平衡，影响模型性能。现有方法如特定损失函数或传统数据增强，存在超参数敏感或仅能修改简单图像特征等局限。本文探索生成式人工智能（GenAI）作为数据扩增的新途径，重点研究Stable Diffusion与CycleGAN在热成像下联合收割机部件分割中的应用。结果表明，采用Stable Diffusion进行数据扩增可带来最佳效果，使分割性能提升4.6%，最终达到84.6%的平均交并比（Mean IoU）。
原文摘要 · Abstract (English)
Supervised machine learning algorithms play a crucial role in optical quality control within industrial production. These approaches require representative datasets for effective model training. However, while non-defective components are frequent, defective parts are rare in production, resulting in highly imbalanced datasets that adversely impact model performance. Existing strategies to address this challenge, such as specialized loss functions or traditional data augmentation techniques, have limitations, including the need for careful hyperparameter tuning or the alteration of only simple image features. Therefore, this work explores the potential of generative artificial intelligence (GenAI) as an alternative method for expanding limited datasets and enhancing supervised machine learning performance. Specifically, we investigate Stable Diffusion and CycleGAN as image generation models, focusing on the segmentation of combine harvester components in thermal images for subsequent defect detection. Our results demonstrate that dataset expansion using Stable Diffusion yields the most significant improvement, enhancing segmentation performance by 4.6 %, resulting in a Mean Intersection over Union (Mean IoU) of 84.6 %.
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