Benchmarking Modern Deep Learning Models for Electroluminescence-Based Solar Cell Defect Detection

This study proposes a deep learning-based framework for the automated classification of photovoltaic solar cells as defective or normal using electroluminescence (EL) imaging. A balanced dataset containing 20,400 EL images, comprising 10,200 defective and 10,200 normal solar cells, was used for mode...

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Bibliografski detalji

Glavni autori:	Gökhan Şahin, Ali Cengiz Rüstemli, Ahmed Yaseen Bishree Al-Ani, Sabir Rüstemli, Erdal Akin
Format:	Artigo
Jezik:	Inglês
Izdano:	MDPI AG 2026-07-01
Serija:	Sensors
Teme:	deep learning photovoltaic cells electroluminescence imaging defect detection EfficientNet-B2 ConvNeXt-Tiny
Online pristup:	https://www.mdpi.com/1424-8220/26/13/4256
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