Fabric Defect Classification Algorithm Based on Multi-Scale Feature Fusion of Spatial Attention

Zhiyong Song; Haipeng Pan

doi:10.3788/LOP202259.1010005

Journals >Laser & Optoelectronics Progress >Volume 59 >Issue 10 >Page 1010005 > Article

Laser & Optoelectronics Progress
Vol. 59, Issue 10, 1010005 (2022)

Fabric Defect Classification Algorithm Based on Multi-Scale Feature Fusion of Spatial Attention

Zhiyong Song and Haipeng Pan^*

Author Affiliations

School of Mechanical and Automatic Control, Zhejiang Sci-Tech University, Hangzhou 310018, Zhejiang , China

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DOI: 10.3788/LOP202259.1010005 Cite this Article Set citation alerts

Zhiyong Song, Haipeng Pan. Fabric Defect Classification Algorithm Based on Multi-Scale Feature Fusion of Spatial Attention[J]. Laser & Optoelectronics Progress, 2022, 59(10): 1010005 Copy Citation Text

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Fig. 1. Overall network structure

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Fig. 2. Fabric defect types of printed fabric data set

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Fig. 3. Training process of printed fabric. (a) Accuracy curve; (b) loss curve

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Fig. 4. Confusion matrix

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Fig. 5. Positioning effect of printed fabric. (a) Original picture; (b) heatmap; (c) location result

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Parameter	Value
Learning rate	0.0001
Batch size	5
Epoch	50
Image dimention	256×256×1

Table 1. Super parameter setting

Defect type	Precision /%	Recall /%	Specificity /%
Average	93.23	92.5	98.5
Normal	82.61	95.00	96.00
Hole	95	95.00	99.00
Stain	100	95.00	100.00
Scratch	100	100.00	100.00
Thread ends	81.82	90.00	96.00
Fold	100	80.00	100.00

Table 2. Defect detection index of each type of printed fabric

Algorithm	Precision /%	Recall /%	Specificity /%	Accuracy /%
VGG16	57.33	56.67	91.33	56.67
ResNet50	49.93	65	93	65
Proposed algorithm	93.23	92.5	98.5	92.5

Table 3. Result comparison between proposed algorithm and other algorithms

Zhiyong Song, Haipeng Pan. Fabric Defect Classification Algorithm Based on Multi-Scale Feature Fusion of Spatial Attention[J]. Laser & Optoelectronics Progress, 2022, 59(10): 1010005

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