English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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-&#x03BC; 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.

Quantification of Damages and Classification of Flaws in Mono-crystalline Photovoltaic Cells through the Application of Vision Transformers