微型电脑应用2025，Vol.41Issue(4)：287-290,4.
上下文关系编码器与孪生神经网络在工业缺陷检测中的应用
The Application of Contextual Relation Encoder and Siamese Neural Network in Industrial Defect Detection
摘要
Abstract
Industrial products are ubiquitous in modern society,but some product quality problems inevitably occur in the indus-trial production process.To address the problems of unbalanced data volume between classes of sample images collected in in-dustrial scenarios and small overall sample size,this paper introduces a CRE-SNN industrial defect classification network com-bining contextual relation encoder(CRE)and siamese neural network(SNN),which does not require labeling of sample data and generates labeled feature images through cyclic iteration.The model can solve the problem of time-consuming labeling.Meanwhile,the network adopts a feature contrast-based classification approach,which can address the challenge of insufficient sample quantities in industrial scenarios.The trained network can be applied to many different datasets and extreme cases with only a few sample images,and has good generalization and robustness.Experiments on three different industrial datasets show that the accuracy and mAP of the proposed network are improved compared with other classical algorithms.关键词
缺陷检测/上下文关系编码器/连体神经网络/小样本学习Key words
defect detection/contextual relation encoder/siamese neural network/few-shot learning分类
信息技术与安全科学引用本文复制引用
张卉婧,李敏波..上下文关系编码器与孪生神经网络在工业缺陷检测中的应用[J].微型电脑应用,2025,41(4):287-290,4.
