Online Detection Method of Woven Bag Defects Based on Machine Vision

Fig. 3. Preprocessing images.(a) Original hole image; (b) mean filtered image of hole; (c) gray close operation image of hole; (d) gray open operation image of hole; (e) original wire drawing; (f) close operation image of wire drawing; (g) original black thing; (h) close operation image of the black thing

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Fig. 4. Images of difference image binarization. (a) Hole image; (b) wire drawing; (c) black thing

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Fig. 5. Diagram of defect detection results. (a) Close operation of the hole image; (b) open operation of the hole image; (c) close operation of the wire drawing; (d) close operation of the black thing

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Fig. 6. Structural factor characteristic diagram

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Fig. 7. Feature maps. (a) Defects of small wire drawing; (b) defects of big wire drawing; (c) defects of hole; (d) defects of fold; (e) defects of the black thing

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Fig. 8. Diagram of defect detection results

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Table 1. Feature and defect classification matching table

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Table 1. Feature and defect classification matching table

No.	Condition	Classification
1	G≥5% and L≥100	Big wire drawing
2	G≥5% and100>L≥30 and S>7	Small wire drawing
3	G<5%	Black stripes
4	G≥5% and 100>L≥30 and S≤7	Black thing
5	A≥200 and 0.1≥D≥-0.5	Hole
6	D<-0.5 or D>0.1	Fold

Table 2. Test classification results
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Table 2. Test classification results
No. Specie Sample Success number Error number
1 Nonconforming product 400 389 11
2 Hole defects 48 46 2
3 Drawing defects 52 51 1
Total 500 486 14