Defect Detection for Solar Cells using Dense Backbone Network Algorithm

Zheng Tang; Huilin Zhang; Lixin Ma

doi:10.3788/LOP222422

Journals >Laser & Optoelectronics Progress >Volume 60 >Issue 14 >Page 1412003 > Article

Laser & Optoelectronics Progress
Vol. 60, Issue 14, 1412003 (2023)

Defect Detection for Solar Cells using Dense Backbone Network Algorithm

Zheng Tang, Huilin Zhang, and Lixin Ma^*

Author Affiliations

School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

show less

DOI: 10.3788/LOP222422 Cite this Article Set citation alerts

Zheng Tang, Huilin Zhang, Lixin Ma. Defect Detection for Solar Cells using Dense Backbone Network Algorithm[J]. Laser & Optoelectronics Progress, 2023, 60(14): 1412003 Copy Citation Text

show less

Fig. 1. Electroluminescence test procedure

Download full size

Defect image classification. (a) Single crystal silicon crack; (b) polysilicon crack; (c) single crystal silicon finger-interruption; (d) polysilicon finger-interruption

Fig. 2. Defect image classification. (a) Single crystal silicon crack; (b) polysilicon crack; (c) single crystal silicon finger-interruption; (d) polysilicon finger-interruption

Download full size

Fig. 3. YOLOv4 network structure

Download full size

Fig. 4. Detection results of the proposed algorithm

Download full size

Fig. 5. Detection results of the YOLOv4 algorithm

Download full size

Fig. 6. AP value comparison

Download full size

Fig. 7. Loss value curve

Download full size

Fig. 8. F1 value of crack

Download full size

Fig. 9. F1 value of finger-interruption

Download full size

Layer	Module	Stride	Output size
Convolution	7×7 Conv	2	800×800
Pooling	7×7 max pooling	2	400×400
Dense block	（1×1 Conv）×6 （3×3 Conv）×6		400×400
Transition layer	1×1 Conv	1	400×400
Transition layer	2×2 average pooling	2	200×200
Dense block	（1×1 Conv）×12 （3×3 Conv）×12		200×200
Transition layer	1×1 Conv	1	200×200
Transition layer	2×2 average pooling	2	100×100
Dense block	（1×1 Conv）×24 （3×3 Conv）×24		100×100
Transition layer	1×1 Conv	1	100×100
Transition layer	2×2 average pooling	2	50×50
Dense block	（1×1 Conv）×16 （3×3 Conv）×16		50×50
Classification layer			1×1

Table 1. DenseNet121 model parameters

Parameter	Value
Input size	800×800
Freeze training epoch	100
Freeze training learning rate	0.001
Unfreeze training epoch	100
Unfreeze training learning rate	0.0001
Label_smoothing	0.005
N_T∶N_V	4∶1

Table 2. Model parameter setting

Algorithm	Defect	TP	FP	FN
Proposed algorithm	Finger-interruption	3148	551	63
Proposed algorithm	crack	1865	325	51
YOLOv4	Finger-interruption	3027	711	184
YOLOv4	crack	1772	474	144
Efficientnet-YOLOv3	Finger-interruption	2907	912	304
Efficientnet-YOLOv3	crack	1645	864	271
YOLOv4-tiny	Finger-interruption	2953	735	258
YOLOv4-tiny	crack	1659	589	257
Faster-rcnn	Finger-interruption	2775	17236	441
Faster-rcnn	crack	1721	10879	195
YOLOv5	Finger-interruption	3097	596	114
YOLOv5	crack	1827	412	89

Table 3. Comparison of detection results of different algorithms

Ghostnet	Mobilenetv1	CSPDarkNet53	DenseNet121	NMS	Softer-NMS	mAP /%	Speed /（frame·s^-1）	TP	FP	FN
		√		√		87.14	22.73	4799	1185	328
			√	√		90.37	30.16	4908	1002	219
		√			√	90.24	19.65	4926	1096	206
			√		√	93.08	27.35	5013	876	114
	√				√	81.95	28.42	4611	1328	516
√					√	84.26	28.87	4685	1127	442

Table 4. Ablation experiment

Algorithm	mAP /%	Speed/（frame·s^-1）	Param /MB
Proposed algorithm	93.08	27.35	160.4
YOLOv4	87.14	22.73	244.6
Efficientnet-YOLOv3	84.02	23.20	154.1
YOLOv4-tiny	83.36	113.5	22.6
Faster-rcnn	63.48	12.06	108.7
YOLOv5	90.21	19.47	335.3

Table 5. Comparison of model indicators

Zheng Tang, Huilin Zhang, Lixin Ma. Defect Detection for Solar Cells using Dense Backbone Network Algorithm[J]. Laser & Optoelectronics Progress, 2023, 60(14): 1412003

Download Citation

Set citation alerts for the article

Tools

Set citation alerts for the article

Save the article for my favorites

Paper Information

微信扫一扫：分享

微信扫一扫：分享