Fabric Defect Detection Based on Computer Vision

2011 ◽  
pp. 86-91 ◽  
Author(s):  
Jing Sun ◽  
Zhiyu Zhou
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
Fabric Defect Detection ◽  
Fabric Defect

Fabric Defect Detection based on Improved Faster RCNN

2021 ◽  
Vol 12 (04) ◽  
pp. 23-32
Author(s):  
Yuan He ◽  
Han-Dong Zhang ◽  
Xin-Yue Huang ◽  
Francis Eng Hock Tay
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Production Process ◽  
Product Quality ◽  
Defect Detection ◽  
Classification Accuracy ◽  
Detection Method ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Computer Vision Technology

In the production process of fabric, defect detection plays an important role in the control of product quality. Consider that traditional manual fabric defect detection method are time-consuming and inaccuracy, utilizing computer vision technology to automatically detect fabric defects can better fulfill the manufacture requirement. In this project, we improved Faster RCNN with convolutional block attention module (CBAM) to detect fabric defects. Attention module is introduced from graph neural network, it can infer the attention map from the intermediate feature map and multiply the attention map to adaptively refine the feature. This method improve the performance of classification and detection without increase the computation-consuming. The experiment results show that Faster RCNN with attention module can efficient improve the classification accuracy.

Fabric Defect Detection Using Computer Vision

2021 ◽  
pp. 35-43
Author(s):  
V. Likith Kumar ◽  
A. Hari Priya ◽  
N. Jahnavi Chakravarthy ◽  
Padarti Vijaya Kumar
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
Fabric Defect Detection ◽  
Fabric Defect

scholarly journals Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Aqsa Rasheed ◽  
Bushra Zafar ◽  
Amina Rasheed ◽  
Nouman Ali ◽  
Muhammad Sajid ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Production Process ◽  
Digital Image Processing ◽  
Defect Detection ◽  
Digital Image ◽  
Textile Industry ◽  
Future Research ◽  
Fabric Defect Detection ◽  
Fabric Defect

There are different applications of computer vision and digital image processing in various applied domains and automated production process. In textile industry, fabric defect detection is considered as a challenging task as the quality and the price of any textile product are dependent on the efficiency and effectiveness of the automatic defect detection. Previously, manual human efforts are applied in textile industry to detect the defects in the fabric production process. Lack of concentration, human fatigue, and time consumption are the main drawbacks associated with the manual fabric defect detection process. Applications based on computer vision and digital image processing can address the abovementioned limitations and drawbacks. Since the last two decades, various computer vision-based applications are proposed in various research articles to address these limitations. In this review article, we aim to present a detailed study about various computer vision-based approaches with application in textile industry to detect fabric defects. The proposed study presents a detailed overview of histogram-based approaches, color-based approaches, image segmentation-based approaches, frequency domain operations, texture-based defect detection, sparse feature-based operation, image morphology operations, and recent trends of deep learning. The performance evaluation criteria for automatic fabric defect detection is also presented and discussed. The drawbacks and limitations associated with the existing published research are discussed in detail, and possible future research directions are also mentioned. This research study provides comprehensive details about computer vision and digital image processing applications to detect different types of fabric defects.

Fabric defect detection based on adaptive LBP and SVM

2010 ◽  
Vol 30 (6) ◽  
pp. 1597-1601 ◽  
Author(s):  
Rong FU ◽  
Mei-hong SHI
Keyword(s):  
Defect Detection ◽  
Fabric Defect Detection ◽  
Fabric Defect

Machine Vision Window Division on Self-Adaptation

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.

Sign in / Sign up

Export Citation Format

Share Document