Journal of Textile Research ›› 2019, Vol. 40 ›› Issue (02): 38-44.doi: 10.13475/j.fzxb.20180607007

• Textile Engineering • Previous Articles     Next Articles

Fabric defect detection based on improved local adaptive contrast method

DU Shuai1, LI Yueyang1(), WANG Mengtao1, LUO Haichi2, JIANG Gaoming1   

  1. 1. Engineering Research Center for Knitting Technology, Ministry of Education, Jiangnan univevsity, Wuxi Jiangsu 214122, China
    2. Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Received:2018-06-25 Revised:2018-08-02 Online:2019-02-15 Published:2019-02-01
  • Contact: LI Yueyang E-mail:lyueyang@jiangnan.edu.cn

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Abstract:

In order to improve the fabric defect detection accuracy and detection effect, a background estimation method based on the most similar neighborhood patch was used to improve the detection rate. Firstly, the image was preprocessed by homomorphic filtering. Then, each pixel of the filtered image was taken as center point and window size of 11 pixel×39 pixel was taken as the central region. By calculation the similarity between the central region and the surrounding neighborhood to find out the neighborhood which was most similar to central region. So then, the purpose of background estimation was achieved. The background-difference principle was used to obtain the target image and the method of threshold segmentation and morphological was used in the image. Finally, the defection results were obtained. The experiment results show that the method is superior to the traditional detection method, not only can detection the defect image in complex background, but also has good detection results for fabric defect images under influence of external factors and different fabric weaves, the detection rate can reach 98%, and with high recognition rate, applicability and a certain degree of anti-interference.

Key words: fabric defect, defect detection, adaptive local contrast method, background difference method, threshold segmentation

CLC Number: 

  • TP391.4

Fig.1

Fabric defect image"

Fig.2

Comparison between original image and homomorphic filtering image. (a)Original image;(b)Homomorphic filtering image"

Fig.3

Principle of detection algorithm"

Fig.4

Fabric defect detection results in complex background. (a) Clip mark;(b) Detection results in clip mark;(c) Fluff;(d) Detection results in fluff; (e) Filling b;(f) Detection results in Filling b"

Fig.5

Compared detection results for several defect detection methods. (a)External fiber; (b) Result of (a) by using method of document[1]; (c) Result of (a) by using method of document[5]; (d)Result of (a);(e)Coarse pick; (f) Result of (e) by using method of document[1]; (g) Result of (e) by using method of document[5]; (h)Result of (e);(i)Filling floats;(j) Result of (i) by using method of document[1]; (k) Result of (i) by using method of document[5]; (l)Result of (i)"

Fig.6

z1 weave defect detection results. (a) Yarn was broken in left;(b) Detection results of (a);(c) Yarn was broken in middle;(d) Detection results of (c);(e) Yarn was broken in right;(f) Detection results of (e)"

Fig.7

z2 weave defect detection results. (a) Yarn was broken in left;(b) Detection results of (a);(c) Yarn was broken in middle;(d) Detection results of (c);(e) Yarn was broken in right; (f) Detection results of (e)"

Fig.8

z3 weave defect detection results. (a) The yarn was broken in left;(b) Detection results of (a);(c) The yarn was broken in middle;(d) Detection results of (c);(e) The yarn was broken in right;(f) Detection results of (e)"

Tab.1

Statistics for defect detection results"

图像类型 检测结果/张 检测率/% 综合检
测率/%
未检测出 检测出 准确率 虚警率
含疵点 2 56 96.55 - 98
不含疵点 0 42 100 0
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