Photovoltaic cell surface defect detection based on DBBR-YOLO

doi:10.11996/JG.j.2095-302X.2024050913

Abstract

Abstract:

A method for detecting surface defects of photovoltaic (PV) cells based on DBBR-YOLO was proposed to address the difficulties in defect feature extraction and the issues of real-time detection and accuracy. Firstly, a diverse branch block (DBB) was incorporated into the C2f module of the YOLOv8n Backbone section to introduce diversified feature extraction paths, enhancing the capability of feature extraction. Secondly, the Neck section of the model was fused with Gold-YOLO to achieve global information aggregation and feature fusion at different hierarchical levels, improving the efficiency of information interaction between feature maps and enhancing the feature expression capability of the model. Finally, the SimAM attention mechanism was introduced to improve the feature expression capability, thereby enhancing the model’s ability to detect small defects or targets. Experiments conducted on five types of PV cell surface defects demonstrated that the improved DBBR-YOLO model achieved an mAP50 value of 93.1%, a 3.7% improvement over YOLOv8n, with an FPS value of 158.0. The performance of the model in terms of accuracy and speed can meet the requirements for real-time detection and accuracy, making it suitable for practical application scenarios of detecting PV cell surface defects.

Key words: photovoltaic cell defects, Gold-YOLO, attention mechanism, deep learning, YOLOv8

CLC Number:

TP391

LIU Yiyan, HAO Tingnan, HE Chen, CHANG Yingjie. Photovoltaic cell surface defect detection based on DBBR-YOLO[J]. Journal of Graphics, 2024, 45(5): 913-921.

Figures/Tables 14

References 22

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配置环境	版本
操作系统	Windows 11
CPU	Intel(R) Core(TM) i7-13700KF
显卡	NVIDIA GeForce RTX 4090 24 GB
深度学习框架	Pytorch
CUDA	CUDA 11.2
Python	Python-3.8.18

配置环境	版本
操作系统	Windows 11
CPU	Intel(R) Core(TM) i7-13700KF
显卡	NVIDIA GeForce RTX 4090 24 GB
深度学习框架	Pytorch
CUDA	CUDA 11.2
Python	Python-3.8.18

超参数	参数值	超参数	参数值
Images size	640	Momentum	0.937
Batch size	32	Epochs	100
Learning rate	0.01	Optimizer	SGD

超参数	参数值	超参数	参数值
Images size	640	Momentum	0.937
Batch size	32	Epochs	100
Learning rate	0.01	Optimizer	SGD

Group	Model	参数量/M	mAP50/%	mAP50:95/%	GFLOPs/G	FPS	F1
1	YOLOv8n	3.1	89.4	66.2	8.1	166.2	0.84
2	+C2f_DBB	3.1	90.6	65.3	8.1	173.5	0.86
3	+RepGDNeck	5.9	91.1	66.8	10.2	108.2	0.88
4	+C2f_DBB+RepGDNeck	5.9	92.3	67.7	10.2	90.4	0.88
5	+C2f_DBB+RepGDNeck+simAM	5.9	93.1	68.2	10.2	158.0	0.89