JOURNAL OF TEXTILE RESEARCH ›› 2018, Vol. 39 ›› Issue (03): 154-160.doi: 10.13475/j.fzxb.20170402007

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Application of algorithm with improved frequency-tuned salient region

  

  • Received:2017-04-10 Revised:2017-10-09 Online:2018-03-15 Published:2018-03-09

Abstract:

In order to improve the efficiency and accuracy of fabric defect detection, a novel defect detection algorithm with improved frequency-tuned salient region (FT) to replace the Gabor wavelet method was proposed to improve the contrast ratio of fabric defection image and enbance the sensitivity of feature vectors. The influence factors of FT algorithm on the recognition precision of fabric defect including Gauss filter template, Lab color space, the salt and pepper noise in the image of the filter and the different ranges of HSV color space were analyzed. The FT algorithm was improved based on the analytic result. The fabric images were highlighted by the improved FT algorithm. Simultaneously, the gray-level co-occurrence matrix method was to extract the feature vector from the highlighted image. Finally, probabilistic neural network was employed to detect the defect on the fabric image. Through the detection of 2 kinds of fabrics with different textures, the experimental results show that the computational time of the improved FT algorithm is prolonged by about 8%. Meanwhile, the accuracy of defect detection increases by 18%~25%. Compared with the Gabormethod, the detection accuracy is substanially the same, but the computation time is shortened by about 70%.

Key words: defect detection, salient map, frequency-tuned, image processing

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