Journal of Textile Research ›› 2019, Vol. 40 ›› Issue (06): 117-124.doi: 10.13475/j.fzxb.20180704708

• Management & Information • Previous Articles     Next Articles

Defect detection algorithm for multiple texture hierarchical fusion fabric

ZHU Hao1, DING Hui1,2,3, SHANG Yuanyuan1,2,3, SHAO Zhuhong1,2,4   

  1. 1. College of Information Engineering, Capital Normal University, Beijing 100048, China
    2. Beijing Engineering Research Center of High Reliable Embedded System, Beijing 100048, China
    3. Beijing Advanced Innovation Center for Imaging Theory and Technology, Beijing 100048, China
    4. Collaborative Innovation Center for Mathematics and Information of Beijing, Beijing 100048, China
  • Received:2018-07-19 Revised:2019-02-26 Online:2019-06-15 Published:2019-06-25
  • Contact: DING Hui

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Abstract: Aim

ing at the false detection and missing detection caused by complexity and diversity of texture distribution in fabric defect detection, considering the periodicity of fabric texture, an algorithm of multi-texture gradation fusion for fabric defect detection was proposed. In the process of testing, firstly, the defect region was subjected to primary positioning and self-adaptive growth by using the Tamura roughness graph of the fabric defect image, then the primary positioned region was mapped to the original fabric image. The primary positioned region was blocked and the local phase quantization (LPQ) texture feature and Tamura texture feature of each image block were extracted, and the two different texture features were fused. The similarity between the fusion feature and the normal block feature was calculated to obtain the similarity image. Finally, the longitude and latitude feature map and the similarity feature map were fused to find the region of the defects in fabric images. The experimental results on TILDA dataset show that the new approach can reduce the redundancy of defect detection and improve the detection efficiency, and can avoid errors and omissions in the process of defect detection.

Key words: defect detection, fabric texture, feature fusion, Tamura feature, local phase quantization feature

CLC Number: 

  • TP399

Fig.1

Roughness image of different window sizes. (a) Original image; (b) A results;(c)B results; (d) C results"

Fig.2

Fabric images with different textures and cycles"

Fig.3

Fabric defect area coarse positioning and adaptive growth"

Fig.4

Defect identification structure image"

Fig.5

Coarse positioning and adaptive growth of defect result. (a) Original image; (b) Defect location image; (c) Adaptive growth image"

Fig.6

Testing results of fabric defect map. (a) Original image; (b) LBP segmentation results; (c) LPQ segmentation results; (d)Longitude and latitude segmentation results; (e) Tamura segmentation results; (f) Literature [17] segmentation results; (g) Paper segmentation results"

Fig.7

Experimental comparison image. (a) Sample 1# original image; (b) LTJ segmentation of sample 1#; (c) Paper segmentation of sample 1#; (d) Sample 2# original image; (e) LTJ segmentation of sample 2#; (f) Paper segmentation of sample 2#; (g) Sample 3# original image; (h) LTJ segmentation of sample 3#; (i) Paper segmentation of sample 3#; (j) Sample 4# original image; (k) LTJ segmentation of sample 4#; (l) Paper segmentation of sample 4#"

Tab.2

Average time of different texture defect detectionss"

织物编号 LBP LPQ 经纬向 Tamura LBP+经纬向 本文
试样1# 14.96 30.97 6.69 7.91 19.81 13.65
试样2# 14.73 31.12 6.72 7.89 19.93 13.15
试样3# 14.60 31.04 6.71 7.92 19.94 12.95
试样4# 15.10 31.26 6.72 7.90 19.74 13.28

Tab.2

Different texture defect detection probability $\ \ \ \ \ \ \ \%$"

织物
编号		 LBP算法 LPQ算法 经纬向算法 LBP+经纬向算法 本文算法
检出率 误检率 检出率 误检率 检出率 误检率 检出率 误检率 检出率 误检率
试样1# 84.72 15.27 85.64 14.35 81.94 18.05 91.66 8.33 93.05 6.94
试样2# 83.33 16.66 83.79 16.20 80.55 19.44 94.44 5.55 93.05 6.94
试样3# 61.11 38.88 63.88 36.11 43.05 56.94 58.33 41.66 65.27 34.72
试样4# 54.16 45.83 56.01 43.98 41.66 58.33 69.44 30.55 72.77 27.77
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