Fabric defect detection is an important and necessary step in textile mills, and many deep learning-based methods have been proposed to perform defect detection and segmentation for fabric images. However, they still suffer from the lack of labor-intensive and high- cost labeled...
论文阅读笔记《Unsupervised fabric defect detection based on a deep convolutional GAN》,程序员大本营,技术文章内容聚合第一站。
The performance of the proposed method has been extensively evaluated by a variety of real fabric samples. The experimental results in comparison with other methods demonstrate its effectiveness in fabric defect detection. 展开 关键词: defect detection fabric inspection generative adversarial network deep ...
Unsupervised fabric defect segmentation using local patch approximationnorm approximationnovelty detectionfabric defectunsupervised segmentationIn this work, a new method based on local patch approximation is presented to address automated defect segmentation on textile fabrics. The proposed method adopts ...
论文阅读笔记《Unsupervised fabric defect detection based on a deep convolutional GAN》 核心思想 本文提出一种基于DCGAN的无监督纺织物缺陷检测算法,本文还是延续了利用DCGAN根据缺陷图像重构无缺陷的图像样本,然后再寻找重构图像和缺陷图像之间的差别,差别超过阈值的判定为缺陷像素,但在实现的细节上还是有很多的...
BTAD (beanTech Anomaly Detection): 2830 images with three different classes, which 1600×1600, 600×600, and 800×600 | 缺陷类型未说明 Fabric(布料) dataset:256×256 fabric images belonging to three patterns: dot, star, and box-patterned fabrics | each class has 25 defect-free and 25 def...
Deep learning technology has been proven applicable in fabric defect detection, but the detection performance relies on the large-scale labeled training sets. However, it is a tedious task to construct these annotated datasets in the industrial production line. To alleviate this issue, an ...
On this account, an unsupervised fabric surface defect detection method using the Progressive Mask Repair Model (PMRM) has been developed. Specifically, PMRM with transformer architecture gathers detailed feature information. In order to pay closer attention to the textural properties of fabrics, the ...
First of all, the PCA approach is used to reduce the dimension of fabric samples, the obtained eigenvector is used as the initial dictionary, and then the dictionary learning method is operated on the defect-free region to get the standard templates. Secondly, the standard templates are ...
This paper presents a multi-stage unsupervised fabric defect detection method based on DCGAN. The method consists of three stages: the GAN training, the encoder training, and the classifier training. The first two stages allow our model to reconstruct the test images. In the image reconstruction ...