改进YOLOv8的光伏电池缺陷检测算法  

Improved photovoltaic cell defect detection for YOLOv8

作　　者：杨丽[1,2] 杨晨晨 杨耿煌[1,2] 段海龙[1,2] 邓靖威 Yang Li;Yang Chenchen;Yang Genghuang;Duan Hailong;Deng Jingwei(School of Automation and Electrical Engineering,Tianjin University of Technology and Education,Tianjin 300222,China;Tianjin Key Laboratory of Information Sensing and Intelligent Control,Tianjin University of Technology and Education,Tianjin 300222,China)

机构地区：[1]天津职业技术师范大学自动化与电气工程学院,天津300222 [2]天津职业技术师范大学天津市信息传感与智能控制重点实验室,天津300222

出　　处：《电子测量技术》2025年第1期92-99,共8页Electronic Measurement Technology

基　　金：天津市教委科研计划项目(2022ZD036)资助。

摘　　要：针对光伏电池缺陷检测在复杂背景下存在的误检、漏检等问题,提出了一种基于改进YOLOv8的光伏电池缺陷检测算法。首先,采用双向特征金字塔网络作为特征融合机制,通过自顶向下和自底向上的路径,实现多尺度特征的有效融合;其次,在颈部网络引入上下文聚合模块,使用不同空洞卷积速率的空洞卷积获取不同感受野的上下文信息,帮助模型更精准地识别微小目标,进而提升模型的目标检测性能;最后,优化边界框损失函数,并不断调试其权重因子,提高模型的收敛速度与效率。实验结果表明,与YOLOv8算法检测网络相比,本文算法的召回率和平均精确度均值分别提高了10.4%、1.8%,检测帧率达到270 fps,保证了实时检测和后续部署的轻量化要求,改进后的算法能在复杂背景下对光伏电池的缺陷进行鲁棒检测。Aiming at the problems of false detection and missing detection in the complex background of photovoltaic cell defect detection,an improved YOLOv8 based photovoltaic cell defect detection algorithm was proposed.Firstly,the bidirectional feature pyramid network is used as the feature fusion mechanism to achieve multi-scale feature fusion through top-down and top-down paths.Secondly,the context aggregation module is introduced into the neck network,and the context information of different receptive fields is obtained by using the cavity convolution of different cavity convolution rates,which helps the model to identify small targets more accurately,and thus improves the target detection performance of the model.Finally,the boundary frame loss function is optimized and its weight factor is adjusted continuously to improve the convergence speed and efficiency of the model.The experimental results show that compared with the detection network of YOLOv8 algorithm,the recall rate and average accuracy are respectively increased by 10.4%and 1.8%,and the detection frame rate reaches 270 fps,ensuring the lightweight requirements of real-time detection and subsequent deployment.The improved algorithm can carry out robust detection of photovoltaic cell defects under complex background.

关 键 词：光伏电池 缺陷检测 YOLOv8 注意力机制 损失函数 

分 类 号：TN41[电子电信—微电子学与固体电子学]