首页|期刊导航|Computer Modeling in Engineering & Sciences|Hybrid CNN Architecture for Hot Spot Detection in Photovoltaic Panels Using Fast R-CNN and GoogleNet

Hybrid CNN Architecture for Hot Spot Detection in Photovoltaic Panels Using Fast R-CNN and GoogleNet

Carlos Quiterio Gómez Muñoz Fausto Pedro García Márquez Jorge Bernabé Sanjuán

Computer Modeling in Engineering & Sciences2025，Vol.144Issue(9)：P.3369-3386,18.

Computer Modeling in Engineering & Sciences2025，Vol.144Issue(9)：P.3369-3386,18.DOI:10.32604/cmes.2025.069225

Hybrid CNN Architecture for Hot Spot Detection in Photovoltaic Panels Using Fast R-CNN and GoogleNet

Carlos Quiterio Gómez Muñoz ¹Fausto Pedro García Márquez ²Jorge Bernabé Sanjuán³

作者信息

1. HCTLab Research Group,Electronics and Communications Technology Department,Universidad Autónoma de Madrid,Madrid,28049,Spain
2. Ingenium Research Group,Universidad de Castilla-La Mancha,Ciudad Real,13071,Spain
3. Department of Engineering,School of Architecture,Engineering and Design,Universidad Europea de Madrid,Villaviciosa de Odon,28670,Spain
折叠

摘要

关键词

Photovoltaic panel/convolutional neural network/deep learning/hot spots/thermal imaging/unmanned aerial vehicle inspection/GoogleNet/fast regions with convolutional neural networks/automated defect detection/transfer learning/aerial thermography

分类

信息技术与安全科学

引用本文复制引用

Carlos Quiterio Gómez Muñoz,Fausto Pedro García Márquez,Jorge Bernabé Sanjuán..Hybrid CNN Architecture for Hot Spot Detection in Photovoltaic Panels Using Fast R-CNN and GoogleNet[J].Computer Modeling in Engineering & Sciences,2025,144(9):P.3369-3386,18.

基金项目

funded by the Spanish Ministerio de Ciencia,Innovación y Universidades,grant number RTC2019-007364-3(FPGM) （FPGM）

by the Comunidad de Madrid through the direct grant with ref.SI4/PJI/2024-00233 for the promotion of research and technology transfer at the Universidad Autónoma de Madrid。（）

Computer Modeling in Engineering & Sciences

ISSN：1526-1492

访问量0

下载量0

段落导航