迁移学习在印品表面缺陷检测中的研究
Research on Transfer Learning in Surface Defect Detection of Printed Products
朱新雨 1司占军 2陈志宇1
作者信息
- 1. 天津科技大学轻工科学与工程学院,天津 300457
- 2. 天津科技大学人工智能学院,天津 300457
- 折叠
摘要
为了推动印刷制造业向智能化发展,解决监督学习面临的工作量大、成本高、泛化差、标注问题等挑战,本研究提出了一种基于迁移学习的无监督印刷缺陷检测方法.这种方法可以在印刷表面进行缺陷检测,而不需要广泛标记缺陷.本研究提出的方法采用了ResNet101-SSTU模型,在印刷缺陷图像的公共数据集上,该模型不仅实现了与主流监督学习检测模型相当的性能和速度,而且成功地解决了监督学习中遇到的一些检测挑战.本研究提出的ResNet101-SSTU模型有效地消除了训练中对大量缺陷样本和标记数据的需求,为印刷行业的质量检测提供了有效的解决方案.
Abstract
To advance the printing manufacturing industry towards intelligence and address the challenges faced by supervised learning,such as the high workload,cost,poor generalization,and labeling issues,an unsupervised and transfer learning-based method for printing defect detection was proposed in this study.This method enabled defect detection in printed surface without the need for extensive labeled defect.The ResNet101-SSTU model was used in this study.On the public dataset of printing defect images,the ResNet101-SSTU model not only achieves comparable performance and speed to mainstream supervised learning detection models but also successfully addresses some of the detection challenges encountered in supervised learning.The proposed ResNet101-SSTU model effectively eliminates the need for extensive defect samples and labeled data in training,providing an efficient solution for quality inspection in the printing industry.
关键词
迁移学习/无监督/缺陷检测/印刷Key words
Transfer learning/Unsupervised/Defect detection/Printing引用本文复制引用
出版年
2024
NETL
NSTL
万方数据