计算机工程与应用2025，Vol.61Issue(11)：325-334,10.DOI:10.3778/j.issn.1002-8331.2404-0005
基于半监督学习的光伏板图像分割算法研究
Research on Photovoltaic Plate Image Segmentation Algorithm Based on Semi-Supervised Learning
摘要
Abstract
The precise identification and segmentation of photovoltaic panels have been a hot topic in the photovoltaic industry in recent years.Most existing photovoltaic panel segmentation technologies are based on semantic segmentation,but the accuracy of semantic segmentation models often depends on the size of the dataset.However,obtaining a large amount of accurately annotated data in practical tasks is not easy;at the same time,photovoltaic panel image data suffers from issues such as weak contrast,blurred boundaries,and complex backgrounds that affect segmentation,making it diffi-cult to simply transfer mainstream semantic segmentation models directly to this task.Therefore,this article proposes a photovoltaic panel segmentation algorithm based on semi-supervised learning.Firstly,an improved semi-supervised learning framework based on FixMatch is proposed,which enables the model to fully explore and utilize the feature information of the perturbation space.Subsequently,a dual branch feature aggregation network based on convolutional neural network(CNN)and Transformer is designed.In the design of the dual branch backbone network,the advantages of CNN and Transformer in extracting local and global features are fully utilized,and the feature aggregation network is designed to fully aggregate the feature information of the two branches,thereby enabling the model to learn multi-level feature infor-mation of the original image to the greatest extent possible.Finally,experiments have shown that on the rooftop distribut-ed photovoltaic dataset with spatial resolutions of 0.1 m,0.3 m,and 0.8 m,the proposed method can achieve an average intersection over union(MIoU)index of 83.74%,82.77%,and 80.73%,respectively,using only 1/32 annotated data.关键词
光伏产业/语义分割/半监督学习/卷积神经网络/TransformerKey words
photovoltaic industry/semantic segmentation/semi-supervised learning/convolutional neural networks/Transformer分类
信息技术与安全科学引用本文复制引用
苏俊,刘智权,唐潮龙..基于半监督学习的光伏板图像分割算法研究[J].计算机工程与应用,2025,61(11):325-334,10.基金项目
福建省自然科学基金(2022J05284) （2022J05284）
厦门理工学院高层次人才项目(YKJ22020R). （YKJ22020R）
