计算技术与自动化2023，Vol.42Issue(4)：33-40,8.DOI:10.16339/j.cnki.jsjsyzdh.202304006
基于改进Vision Transformer的光伏电池缺陷识别研究
Defect Recognition of Photovoltaic Cells Based on Convolutional Neural Network and Scaled Vision Transformer
摘要
Abstract
Photovoltaic cells are the core component of solar power generation system.The defects such as wear and crack of the cells not only affect the battery life,but also reduce the energy conversion efficiency.Traditional manual defect detection method is time-consuming and inefficient.This paper designs a defect detection model of photovoltaic cells based on CNN and scaled Vision Transformer(ViT).Firstly,since ViT cannot perceive the global information of the image due to the segmentation of the input image,a residual network containing 12 convolution layers is designed,which is combined with the feature pyramid network to obtain the features of different scales of the input image,and the feature map is segmented and pooled as the input information of Transformer encorder.Secondly,since the deficiency of Transformer position coding function designed manually,a position coding branch module is designed to realize position self-coding.The experiment re-sults on the defect image dataset show that the proposed model improves the accuracy without increasing the amount of cal-culation.关键词
卷积神经网络/注意力机制/Transformer/缺陷识别Key words
convolutional neural network/attention mechanism/Transformer/defective detection分类
信息技术与安全科学引用本文复制引用
吕潇涵..基于改进Vision Transformer的光伏电池缺陷识别研究[J].计算技术与自动化,2023,42(4):33-40,8.
