Fabric Defect Detection Method Based on Gabor Filters

2011 ◽  
Vol 301-303 ◽  
pp. 229-234 ◽  
Author(s):  
Yan Bei Liu ◽  
Zhi Tao Xiao ◽  
Fang Zhang ◽  
Jun Wu
Keyword(s):  
Defect Detection ◽  
Filter Bank ◽  
Detection Method ◽  
Gabor Filter ◽  
Texture Feature ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Gabor Filter Bank ◽  
Detection Effect ◽  
Good Detection

In this paper, a fabric defect detection method based on Gabor filter bank is present. In this method, the parameters of Gabor filter bank depend on the fabric texture feature. Using the Gabor filter bank with multi-frequency and multi-orientation, a textile image produces multi-image. Then, the images are reconstructed into one image for detecting defects. It is illustrated that most kinds of defects are correctly detected and segmented. The experimental results show that the algorithm is robust and has good detection effect.

Fault Automatic Detection Method of Steel Cord Conveyor Belt Based on Gabor Filter Bank

2013 ◽  
Vol 411-414 ◽  
pp. 1218-1221
Author(s):  
Yan Zhang ◽  
Wei Gong ◽  
Wei Liang Zhong
Keyword(s):  
Filter Bank ◽  
Detection Method ◽  
Gabor Filter ◽  
Automatic Detection ◽  
Conveyor Belt ◽  
Gabor Filters ◽  
Fusion Images ◽  
Steel Cord ◽  
Gabor Filter Bank ◽  
Good Detection

A method based on Gabor filter bank is presented to automatically detect the steel cord conveyor belt fault. Firstly, multi-channel filtering is implemented by Gabor filters. Then the characteristic differential and fusion images are calculated. Finally, the fault images are identified by white pixels statistics after the threshold segmentation. The results show that this method has good detection result for steel cord conveyor belt fault.

scholarly journals Automatic Fabric Defect Detection Method Using PRAN-Net

2020 ◽  
Vol 10 (23) ◽  
pp. 8434
Author(s):  
Peiran Peng ◽  
Ying Wang ◽  
Can Hao ◽  
Zhizhong Zhu ◽  
Tong Liu ◽  
...  
Keyword(s):  
Defect Detection ◽  
Detection Method ◽  
Ground Truth ◽  
Inspection Time ◽  
Feature Maps ◽  
Multi Scale ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Feature Pyramid ◽  
Quality Process

Fabric defect detection is very important in the textile quality process. Current deep learning algorithms are not effective in detecting tiny and extreme aspect ratio fabric defects. In this paper, we proposed a strong detection method, Priori Anchor Convolutional Neural Network (PRAN-Net), for fabric defect detection to improve the detection and location accuracy of fabric defects and decrease the inspection time. First, we used Feature Pyramid Network (FPN) by selected multi-scale feature maps to reserve more detailed information of tiny defects. Secondly, we proposed a trick to generate sparse priori anchors based on fabric defects ground truth boxes instead of fixed anchors to locate extreme defects more accurately and efficiently. Finally, a classification network is used to classify and refine the position of the fabric defects. The method was validated on two self-made fabric datasets. Experimental results indicate that our method significantly improved the accuracy and efficiency of detecting fabric defects and is more suitable to the automatic fabric defect detection.

Sign in / Sign up

Export Citation Format

Share Document