Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (08): 39-44.doi: 10.13475/j.fzxb.20191000606

• Textile Engineering • Previous Articles     Next Articles

Yarn-dyed fabric defect detection based on context visual saliency

ZHOU Wenming, ZHOU Jian, PAN Ruru()   

  1. College of Textile Science and Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Received:2019-10-08 Revised:2020-05-04 Online:2020-08-15 Published:2020-08-21
  • Contact: PAN Ruru E-mail:prrsw@163.com

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Abstract:

In order to facilitate the effective detection of yarn-dyed fabric defects, a defect detecting method based on context visual saliency was proposed. Using this method, the fabric image was firstly divided into image patches of the same size according to the principle of context visual saliency. Following that, for every image patch, a number (K) of image patches, most similar to the concerned image patch were selected, and the sum of the differences among the K image patches and the image patch of concern were calculated. The calculated sum of the differences was then used to represent the saliency of center pixel of the image patches, thereby generating a visual saliency map. Finally, the threshold of the saliency map was segmented to obtain the detection result of the yarn-dyed fabric defect. In order to verify the validity of the algorithm, the yarn-dyed fabric regional defect image samples with looped weft, holes and netting of color dots, color stripes and color checks were detected. The experimental results show that the proposed algorithm can suppress the texture background and highlight the defect area of different types of fabrics and achieve the effective detection of fabric defects, which indicates the effectiveness of the method for detecting defects in yarn-dyed fabrics.

Key words: defect detection, visual saliency, yarn-dyed fabric, threshold segmentation

CLC Number: 

  • TS941.26

Fig.1

Yarn-dyed fabric sample images. (a) Normal image; (b) Defect image"

Fig.2

Single scale saliency detection of strip defect"

Fig.3

Multi-scale saliency detection of strip defects. (a) Scale 2; (b) Scale 3; (c) Scale 4"

Fig.4

Mean of saliency at four scales detection of strip defect"

Fig.5

Final saliency detection of strip defect"

Fig.6

Saliency maps generated by method of this paper. (a) Yarn-dyed fabric defect images; (b) Visual saliency maps"

Fig.7

Detection result of defect positioning. (a) Binary images; (b) Positioning results"

Fig.8

Visual saliency maps. (a) Defect image; (b) Normal image; (c) Visual saliency map of defect image; (d) Visual saliency map of normal image"

Tab.1

Statistics for defect detection results"

图像类型 检出张数 未检出张数 检测准确率/%
疵点图像 43 2 97.4
正常图像 32 0

Fig.9

Comparison of different visual saliency methods. (a) Yarn-dyed fabric defect images; (b) Method of refe-rence[15]; (c) Method of reference [10]; (d) Method of reference [16]; (e) Method of this paper"

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