Research on Fabric Defect Detection Algorithm Based on Lightweight YOLOv7-Tiny

Figures & data

Figure 1. The YOLOv7-tiny network structure.

Figure 2. YOLOv7-tiny-MGCK network structure diagram.

Figure 3. Conventional convolution module and ghost convolution module.

Figure 4. CARAFE upsampling module.

Table 1. Calculation steps of the KMMP clustering algorithm.

Steps of KMMP clustering algorithm
step1	From the dataset, a sample is chosen at random to serve as the first clustering center ($c_1$).
Step2	First, D(x), which represents the distance in 1-IOU between each sample and the current clustering center, is determined. Next, $P_1$ is used to compute the likelihood that each sample will be chosen as the following clustering center. As the next clustering center, the point with the highest probability is finally chosen.
Step3	Up till K clustering centers are chosen, repeat the previous procedure.
step4	Calculate the 1-IOU distances for each sample $x_i$ in the dataset and group them into the class that corresponds to the cluster center to which they have the closest distance.
Step5	the cluster center $c_i$ for each category i should be recalculated.
Step6	When the clustering center position no longer shifts, repeat Steps 2 and 3 (you can alternatively specify the number of iterations).

Figure 5. Schematic diagram of image acquisition device.

Figure 6. Fabric defect samples in the dataset.

Figure 7. LabelImag annotation example.

Table 2. Experimental environment information table.

	Hardware	Software
CPU	12th Gen Intel(R) Core(Tm) i7-12700H 2.70GHz	Operating system	Windows11
GPU	NVIDIA GeForce RTX 3050 Ti Laptop,4GB	Deployment environment	Python3.8
RAM	DDR5 16GB	Deep learning frameworks	Pytorch1.13
		CUDA version	11.7

Table 3. Parameter setting.

Parametric	Wrth
weight	none
epoch	400
batch_size	4
learning_rate	0.01
momentum	0.937
weight_decay	0.0005

Table 4. Experimental results of different activation functions.

Activation function	P (%)	R(%)	F1	mAP@0.5%)
LeakyReLU(0.1)	90.7	85.5	0.880	92.7
SiLU	93.1	90.2	0.916	95.1
HardSwish	92.5	89.4	0.909	94.2
Mish	93.1	90.7	0.919	95.3

Table 5. Experimental results of different lightweighting methods.

Methods	P(%)	R(%)	F1	mAP@0.5%)	Params(M)	FLOPs(G)
unmodified	93.1	90.7	0.919	95.3	6.029	13.2
Using Ghost Convolution	90.0	92.6	0.913	94.9	3.263	7.8
Using shufflenetv2	89.02	86.89	0.879	91.90	4.178	6.9
Using mobilenetv3	64.60	76.20	0.699	71.49	4.502	8.5

Table 6. Experimental results of different upsampling modules.

$(k_{\mathit{encoder}},k_{\mathit{up}})$	$(k_{\mathit{encoder}},k_{\mathit{up}})$	P (%)	R(%)	F1	mAP@0.5 (%)	Params
		90.7	85.5	0.880	92.7
(3,5)	(3,5)	90.3	85.1	0.876	906	128144
(1,3)	(1,3)	92.4	87.9	0.901	93.7	17296
(1,5)	(1,5)	89.1	84.3	0.866	92.0	25744
(1,3)	(1,5)	95.1	86.3	0.905	94.1	21520
(1,5)	(1,3)	91.0	91.1	0.910	93.9	21520

Figure 8. Comparison of bounding box loss using KMMP clustering algorithm and original algorithm.

Table 7. YOLOv7-tiny-MGCK model ablation experiment results.

Arithmetic	Mish	Ghost	CARAFE	KMMP	P (%)	R (%)	F1	mAP@0.5 (%)	Params (M)	GLOPs (G)
YOLOv7-tiny					90.7 (base value)	85.5 (base value)	0.880 (base value)	92.7 (base value)	6.029 (base value)	13.2 (base value)
YOLOv7-tiny-M	√				93.1	90.7	0.919	95.3	6.029	13.2
YOLOv7-tiny-G		√			91.2	91.6	0.914	94.6	3.263	7.8
YOLOv7-tiny-C			√		95.1	86.3	0.905	94.1	6.050	13.2
YOLOv7-tiny-K				√	90.2	90.1	0.901	93.5	6.029	13.2
YOLOv7-tiny-MG	√	√			90.0	92.6	0.913	94.9	3.263	7.8
YOLOv7-tiny-MGC	√	√	√		95.5	90.9	0.931	95.7	3.284	7.8
YOLOv7-tiny-MGCK	√	√	√	√	94.6 (+3.9%)	92.5 (+7.0%)	0.935 (+5.5%)	96.4 (+3.7%)	3.284 (−45.5%)	7.8 (−41.0%)

*(’±’ shows several increases or decreases in the base value.).

Figure 9. YOLOv7-tiny detection results.

Figure 10. YOLOv7-tiny-MGCK detection results.

Table 8. Performance comparison of different algorithms.

Algorithm	Precision (%)	Recall (%)	F1	mAP@0.5 (%)	Parameters (M)	Weight (MB)	Detect speed (FPS)
Faster R-CNN	-	-	0.608	66.9	-	-	12
SSD	-	-	0.364	44.7	-	-	27
YOLOv3-tiny	66.6	74.0	0.701	66.3	8.68	16.6	140
YOLOv3	95.6	90.5	0.930	96.0	61.52	117	25
YOLOv5s	90.8	92.3	0.915	94.2	7.03	13.7	108
YOLOv5l	92.9	93.6	0.932	95.1	46.14	92.9	33
YOLOv7-tiny	90.7	85.5	0.880	92.7	6.03	11.7	144
YOLOv7-tiny-MGCK(ours)	94.6	92.5	0.935	96.4	3.28	6.6	109