Quantification of Damages and Classification of Flaws in Mono-Crystalline Photovoltaic Cells Through the Application of Vision Transformers.
IEEE access(2023)
摘要
This work introduces new effective methodologies for the detection, analysis, and classification of diverse defects that may occur throughout the production process of photovoltaic panels. In this context, this work proposes a novel approach that combines Image Processing and Vision Transformers (ViT) to address this challenge. The results of this work comprise a light flaw-type classifier based on ViT, along with computational tools to calculate the length of cracks and the proportional damaged area caused by flaws without requiring the training of other models. The proposed ViT-μ model achieved high accuracy in flaw detection and classification for solar cells, with rates of nearly 98% and 94%, respectively; achieved with a mere one-hour training duration. Moreover, this study introduces a weakly supervised method of visualizing the detected defects within a solar cell, by using attention maps.
更多查看译文
关键词
Image processing,photovoltaic cells,visual transformers,machine learning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要