基于小样本度量迁移学习的表面缺陷检测

将小样本学习中的度量学习方法引入缺陷检测领域,提出小样本度量迁移学习方法,用于解决深度学习方法中需要大量学习样本的问题.方法主要分为两个阶段:第一阶段使用公开或便于获得的大型数据集预训练深度网络;第二阶段将网络学习到的相关知识通过度量学习模块迁移到表面缺陷检测领域.实验表明,小样本学习在缺陷检测领域的可行性.

Few-Shot Metric Transfer Learning Network for Surface Defect Detection

Metric learning method of few-shot learning is introduced into the field of defect detection, and a few-shot learning method based on transfer metric learning is proposed to meet the requirement of deep learning method for a large number of learning samples. In the first stage, the deep network is pre-trained on the large datasets which are open or easy to be obtained. In the second stage, the relevant knowledge learned by the network is transferred to the field of surface defect detection through the metric learning module.Experiments show the feasibility of few-shot learning in defect detection.