基于深度学习的光伏板缺陷分类定位算法研究  

Research on Deep Learning⁃based Classification and Localization Algorithm for Photovoltaic Panel Defects

作　　者：刘枭雄 郑茜颖[1] LIU Xiaoxiong;ZHENG Qianying(College of Physics and Information Engineering,Fuzhou University,Fuzhou 350108,CHN)

机构地区：[1]福州大学物理与信息工程学院,福州350108

出　　处：《光电子技术》2024年第1期54-60,共7页Optoelectronic Technology

基　　金：福建省科技重点产业引导项目(2020H0007)。

摘　　要：提出了一种基于深度学习技术的光伏板缺陷分类定位方法,用于快速准确地确定光伏板缺陷的位置和类型。为了克服传统单张图像缺陷检测方法的视角限制,采用图像配准、拼接等算法生成高分辨率的光伏全景图像,并使用深度学习技术对光伏板红外图像进行缺陷分类,通过与可见光图像进行对比,可以有效地确定光伏板缺陷的类型。光伏板缺陷分类的准确率、精确率、召回率和F1分数分别达到了93.71%、93.13%、93.20%和93.11%。与传统方法相比,该方法具有非接触、高效和快速等优点,适用于大规模光伏板缺陷的检测和定位,能够在短时间内获取准确、全面的光伏板缺陷信息。A deep learning-based method was proposed,for defect classification and localization of photovoltaic panels to quickly and accurately determine the location and type of defects.To overcome the perspective limitations of traditional single-image defect detection methods,algorithms were adopted,such as image registration and stitching to generate high-resolution panoramic images of the photovoltaic panels.Deep learning techniques were then used to classify the infrared images of the photovoltaic panels and effectively identify the types of defects by comparing them with visible light images.The accuracy,precision,recall,and F1 score of the photovoltaic panel defect classification could reach 93.71%,93.13%,93.20%,and 93.11%,respectively.Compared with traditional methods,this approach had advantages,such as non-contact,high efficiency,and fast speed,making it suitable for detecting and locating defects in large-scale photovoltaic panels.It could provide accurate and com‐prehensive information about photovoltaic panel defects in a short time.

关 键 词：光伏板 缺陷 深度学习 拼接 缺陷分类 

分 类 号：TN219[电子电信—物理电子学] TP391.41[自动化与计算机技术—计算机应用技术]