Fabric Defect Detection Based on SRG-PCNN

2010 ◽  
Vol 148-149 ◽  
pp. 1319-1326 ◽  
Author(s):  
Xiao Shu Si ◽  
Hong Zheng ◽  
Xue Min Hu
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Defect Detection ◽  
Focal Point ◽  
Region Growing ◽  
Segmentation Method ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Defect Image ◽  
Validation Tests

Defect segmentation has been a focal point in fabric inspection research, and it remains challenging because it detects delicate features of defects complicated by variations in weave textures and changes in environmental factors. According to the different features between the normal fabric image and defect image, this paper presents an adaptive image segmentation method based on a simplified region growing pulse coupled neural network (SRG-PCNN) for detecting fabric defects. The validation tests on the developed algorithms were performed with fabric images, and results showed that SRG-PCNN is a feasible and efficient method for defect detection.

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

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.

Fabric Defect Detection Using Convolutional Neural Network

2020 ◽  
Vol 12 (05-SPECIAL ISSUE) ◽  
pp. 950-955
Author(s):  
Eldho Paul ◽  
Nivedha K ◽  
Nivethika M ◽  
Pavithra V ◽  
Priyadharshini G
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Detection ◽  
Fabric Defect Detection ◽  
Fabric Defect

Segmentation of Fabric Defect Images Based on PCNN Model and Symmetric Tsallis Cross Entropy

2013 ◽  
Vol 760-762 ◽  
pp. 1472-1476 ◽  
Author(s):  
Hong Wan ◽  
Yi Quan Wu ◽  
Zhao Qing Cao ◽  
Zhi Long Ye
Keyword(s):  
Defect Detection ◽  
Cross Entropy ◽  
Segmentation Method ◽  
Fabric Defect Detection ◽  
Segmentation Methods ◽  
Otsu Method ◽  
Defect Area ◽  
Fabric Defect ◽  
Iteration Number ◽  
Strength Difference

Segmentation of defect images is an important step in the automatic fabric defect detection. In order to extract fabric defects effectively, a segmentation method of fabric defect images based on pulse coupled neural network (PCNN) model and symmetric Tsallis cross entropy is proposed. The image is segmented by PCNN according to the gray strength difference between fabric defect area and non-defect area. To guarantee that the grayscale inside the object and background is uniform after segmentation, symmetric Tsallis cross entropy is used as the image segmentation criterion to select the optimal threshold and iteration number. A large number of experimental results show that, compared with the related segmentation methods such as Otsu method, PCNN method, the method based on PCNN and cross entropy, the segmentation effect of the proposed method is the best. The texture of non-defect area is removed more completely, and the defect area is segmented more accurately.

Defect Detection Study of Fabric Based on Gabor Filter Algorithm

2011 ◽  
Vol 697-698 ◽  
pp. 491-494
Author(s):  
G.X. Li ◽  
Y.F. Li
Keyword(s):  
Iterative Algorithm ◽  
Defect Detection ◽  
Gabor Filter ◽  
Detection Algorithm ◽  
Algorithm Analysis ◽  
Post Processing ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Multichannel Data ◽  
Defect Image

This thesis exploits a multichannel Gabor filters detection algorithm. Analysis filtering images from different orientations and scales, then fuses the multichannel data. Finally, a threshold iterative algorithm and mathematical morphology post-processing is used to achieve the fabric defect detection. The experiment selects five types of fabric defect image. Experimental results suggest that this algorithm can effectively identify blob-shaped, linear and planar defect and has well real-time character.

scholarly journals Fabric Defect Detection Using Activation Layer Embedded Convolutional Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 70130-70140 ◽  
Author(s):  
Wenbin Ouyang ◽  
Bugao Xu ◽  
Jue Hou ◽  
Xiaohui Yuan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Detection ◽  
Fabric Defect Detection ◽  
Fabric Defect
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