Inspection of Cotton Woven Fabrics Produced by Ethiopian Textile Factories Through a Real-Time Vision-Based System

Figures & data

Figure 1. The fabric image captured using a digital camera (a) defect-free (b) hole (c) miss pick and (d) double pick.

Figure 2. A structure of automated fabric inspection systems flow chart.

Table 1. Precision parameter definitions.

	Positive	Negative
True	TP	TN
False	FP	FN

Figure 3. The accuracy performance of the AlexNet model.

Figure 4. The performance of the newly proposed CNN model: (a) model accuracy, (b) loss of a proposed model.

Figure 5. Double-pick fabric defect with low-intensity of patterned defect.

Table 2. The confusion matrix of the newly proposed CNN model for fabric defect identification system.

Automatic Fabric Defect Recognition System Confusion Matrix
	1	53 17.00%	0 0%	0 0%	8 2.60%	86.90% 13.10%
Output Class	2	1 0.30%	94 30.10%	1 0.30%	0 0%	97.90% 2.10%
	3	0 0%	1 0.30%	59 18.90%	19 6.10%	74.70% 25.30%
	4	5 1.60%	0 0%	2 0.60%	69 22.10%	90.80% 9.20%
		89.80% 10.20%	98.90% 1.10%	95.20% 4.80%	71.90% 28.10%	88.10% 11.90%
		1	2	3	4
		Target Class

Figure 6. The accuracy comparison of CNN model: VGGNet, AlexNet, and the newly proposed model.

Figure 7. The comparison chart of the VGGNet, AlexNet, and newly proposed model using computational time.