Machine Vision Window Division on Self-Adaptation

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.460-461.617 ◽

2011 ◽

Vol 460-461 ◽

pp. 617-620

Author(s):

Xiu Chen Wang

Keyword(s):

Image Analysis ◽

Machine Vision ◽

Defect Detection ◽

Actual Situation ◽

Adaptive Window ◽

Fabric Defect Detection ◽

Fabric Defect ◽

Self Adaptation ◽

Recognition Efficiency ◽

Division Model

Aiming at time-consuming and ineffective problem of image window division in fabric defect detection, this paper proposes a new adaptive division method after a large number of experiments. This method can quickly and exactly recognize defect feature. Firstly, a division model on adaptive window is established, secondly, the formula to anticipate generally situation of fabric image is given according to the peaks and valleys change in the model, and methods to calculate the division size and position of adaptive window are given. Finally, we conclude that the algorithm in this paper can quickly and simply select the size and position of window division according to actual situation of different fabric images, and the time of image analysis is shortened and the recognition efficiency is improved.

Download Full-text

A Machine Vision-based Fabric Defect Detection Solution for Textile Production Industry Using Object Detection

10.1109/ccdc52312.2021.9601900 ◽

2021 ◽

Author(s):

Shuo Xin ◽

Chunhui Zhao ◽

Youxian Sun

Keyword(s):

Machine Vision ◽

Object Detection ◽

Defect Detection ◽

Textile Production ◽

Fabric Defect Detection ◽

Fabric Defect ◽

Production Industry

Download Full-text

Fabric Defect Detection System Using Stacked Convolutional Denoising Auto-Encoders Trained with Synthetic Defect Data

Applied Sciences ◽

10.3390/app10072511 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2511

Author(s):

Young-Joo Han ◽

Ha-Jin Yu

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Machine Vision ◽

Image Classification ◽

Defect Detection ◽

Detection System ◽

Image Detection ◽

Fabric Defect Detection ◽

Fabric Defect ◽

Segmentation Image

As defect detection using machine vision is diversifying and expanding, approaches using deep learning are increasing. Recently, there have been much research for detecting and classifying defects using image segmentation, image detection, and image classification. These methods are effective but require a large number of actual defect data. However, it is very difficult to get a large amount of actual defect data in industrial areas. To overcome this problem, we propose a method for defect detection using stacked convolutional autoencoders. The autoencoders we proposed are trained by using only non-defect data and synthetic defect data generated by using the characteristics of defect based on the knowledge of the experts. A key advantage of our approach is that actual defect data is not required, and we verified that the performance is comparable to the systems trained using real defect data.

Download Full-text