论文标题
基于少量标记数据的工业产品表面缺陷检测的调查
A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data
论文作者
论文摘要
基于视觉感知的表面缺陷检测方法已广泛用于工业质量检查。因为缺陷数据不容易获得，并且大量缺陷数据的注释会浪费大量的人力和物质资源。因此，本文根据少数标记的数据回顾了工业产品的表面缺陷检测方法，该方法分为传统的基于图像处理的工业产品表面缺陷检测方法和基于深度学习的工业产品表面缺陷检测方法，适用于少量标记的数据。传统的基于图像处理的工业产品表面缺陷检测方法被分为统计方法，光谱方法和模型方法。基于基于少数标记数据的少数标记数据的深度学习工业产品表面缺陷检测方法基于数据扩展，基于转移学习，基于模型的微调，半监督，弱监督和无人监督。
The surface defect detection method based on visual perception has been widely used in industrial quality inspection. Because defect data are not easy to obtain and the annotation of a large number of defect data will waste a lot of manpower and material resources. Therefore, this paper reviews the methods of surface defect detection of industrial products based on a small number of labeled data, and this method is divided into traditional image processing-based industrial product surface defect detection methods and deep learning-based industrial product surface defect detection methods suitable for a small number of labeled data. The traditional image processing-based industrial product surface defect detection methods are divided into statistical methods, spectral methods and model methods. Deep learning-based industrial product surface defect detection methods suitable for a small number of labeled data are divided into based on data augmentation, based on transfer learning, model-based fine-tuning, semi-supervised, weak supervised and unsupervised.
