基于改进SAE网络的织物疵点检测算法

针对传统织物缺陷检测手工提取特征困难,疵点样本有限的问题,结合卷积自编码器(CAE),提出一种基于Fisher准则的栈式去噪自编码器算法(FSDAE)。首先从原始图像中截取若干小块图像,采用稀疏自编码器(SAE)训练,得到小块图像的稀疏性特征;其次利用该特征,初始化CAE网络参数,提取原始图像的低维特征;最后将该特征数据送入FSDAE网络进行疵点检测分类。分别对3类织物进行测试,实验结果表明,算法能够有效地提取织物图像的分类特征,且通过加入Fisher准则,提高了织物疵点的检测率。

Fabric defect detection algorithm based on improved SAE neural network

In this paper,with the combination of convolutional autoencoders (CAE),an algorithm named stacked denoising autoencoders based on Fisher criterion (FSDAE) is proposed to solve the problem of the difficulty of manual features extraction and the limitation of defect samples on traditional fabric defect detection.Firstly,the sparse autoencoder (SAE) is used to obtain the sparse characteristics of the small patches cut out from the original images.Secondly,the CAE network parameters are initialized by using the sparse characteristics and the low-dimensional features of the original image are extracted.Finally,the features data are sent to the FSDAE network for defect detection and classification.The experimental results show that the algorithm can effectively extract the classification characteristics of the fabric image,and the detection rate of the fabric defect is improved by adding the Fisher criterion.