Machine-Vision Based Defect Detection Algorithm for Packaging Bags

Fig. 8. Partial detection results of defect classification. (a) Qualified image; (b) continuous bag (over length); (c) continuous bag (over width); (d) motion of packaging layout; (e) dimension error; (f) foreign matter on packages

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Table 1. Feature and defect matching

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Table 1. Feature and defect matching

No.	Condition	Defect classification
1	L>L_up or W>W_up	Defect 1: continuous bag
2	L>L_up or L<L_low or W>W_up or W<W_low	Defect 2: dimension error(over length or over width)
3	θ<θ_T	Defect 3: foreign matteron packages
4	O⊄M	Defect 4: motion ofpackaging layout

Table 2. Confusion matrix
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Table 2. Confusion matrix
Classification Detection
Defectnumber Qualifiednumber
Actual Defect number P_T N_F
Qualified number P_F N_T

Table 3. Confusion matrices for different detection methods
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Table 3. Confusion matrices for different detection methods
Category Proposedmethod Templatematching Manualdetection
Defect Qualified Defect Qualified Defect Qualified
Defect 195 5 178 22 186 14
Qualified 6 294 25 275 16 284

Table 4. True positive rates, true negative rates, and accuracy of different detection methods
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Table 4. True positive rates, true negative rates, and accuracy of different detection methods
Method True positiverate /% True negativerate /% Accuracy /%
Proposed method 97.5 98 97.8
Template matching 89 91.7 90.6
Manual detection 93 94.7 94

Table 5. Test of classification results

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Table 5. Test of classification results

No.	Defecttype	Sample	Successnumber	Missingnumber	Wrongnumber	Missingrate /%	Errorrate /%	Positiverate /%
1	Continuous bag	200	199	0	1	0	0.5	99.5
2	Dimension error	200	199	0	1	0	0.5	99.5
3	Foreign matter on packages	200	195	1	4	0.5	2.0	97.5
4	Motion of packaging layout	200	197	0	3	0	1.5	98.5
	Total	800	790	1	9	0.125	1.125	98.75

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Dan Li, Guojun Bai, Yuanyuan Jin, Yan Tong. Machine-Vision Based Defect Detection Algorithm for Packaging Bags[J]. Laser & Optoelectronics Progress, 2019, 56(9): 091501

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