基于改进YOLOv5s太阳能电池片表面缺陷检测算法  

Solar cells surface defect detection algorithm based on improved YOLOv5s

作　　者：王巍[1] 余欣[1] 缪佳欣 刘晓宇[1] Wang Wei;Yu Xin;Miu Jiaxin;Liu Xiaoyu(School of Mechanical Engineer,Sichuan University,Chengdu 610000,China)

机构地区：[1]四川大学机械工程学院,成都610000

出　　处：《电子测量技术》2025年第5期128-136,共9页Electronic Measurement Technology

摘　　要：太阳能电池片表面缺陷的出现会严重影响太阳能转化效率,准确检测太阳能电池片表面缺陷并及时处理可以有效提高发电效率。针对太阳能电池片生产过程中表面缺陷检测高精度、实时性的需求,本文提出了一种基于改进的YOLOv5s的太阳能电池片表面缺陷检测算法。该算法首先在主干特征提取网络中用C3CA模块替换网络中C3模块,并加入CBAM注意力机制,提升网络的特征提取能力;其次,在特征融合网络中引入BiFPN网络结构,提升网络中不同语义和尺度信息的特征融合能力;最后,在输出端引入解耦头,提高了模型网络的收敛速度与检测精度。实验结果表明:改进模型在光伏电池EL数据集上平均精度均值mAP@0.5∶0.95为66.4%,相较于原网络提高了7.1%,实现了对太阳能电池片表面缺陷的快速有效定位识别,在太阳能电池工业生产过程中具有一定的实际应用价值。The appearance of defects on the surface of solar cells will seriously affect the efficiency of solar energy conversion,accurate detection of defects on the surface of solar cells and timely treatment can effectively improve power generation efficiency.Aiming at the demand for high precision and real-time surface defect detection in the solar cell production process,this paper proposes a solar cell surface defect detection algorithm based on the improved YOLOv5s.The algorithm firstly replaces the C3 module in the network with a C3CA module in the backbone feature extraction network,and incorporates the CBAM attention mechanism to improve the feature extraction capability of the network;secondly,introduces the BiFPN network structure in the feature fusion network to improve the feature fusion capability of different semantic and scale information in the network;lastly,introduces a decoupled head in the output end,which improves the convergence speed of the modeled network with the detection accuracy.The experimental results show that the improved model has an average precision of 66.4%on the EL dataset of photovoltaic cells with an mAP@0.5∶0.95,which is 7.1%higher than the original network.It realizes the fast and effective localization and identification of defects on the surface of solar cell wafers,which has certain practical application value in the industrial production process of solar cells.

关 键 词：太阳能电池片 YOLOv5s 缺陷检测 注意力机制 BiFPN 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TM914.4[自动化与计算机技术—计算机科学与技术] TN36[电气工程—电力电子与电力传动]