Weak light detection of photovoltaic panels

Currently, three main technologies are used to detect defects in PV cells: electroluminescence (EL), infrared thermography (IRT), and photoluminescence (PL). This paper presents a defect analysis and performance evaluation of photovoltaic (PV) modules using qu...

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A novel deep learning model for defect detection in photovoltaic

To address the current limitations of low precision and high image data requirements in defect detection algorithms based on visible light imaging, this paper proposes a novel visible light

Accurate detection of bright spots in electro-luminescence images of

After extensive benchmarking against state-of-the-art methods, this paper proposes a robust approach for reliable bright spot detection based on image classification using novel features

A lightweight and efficient model for photovoltaic panel defect

Within this research, we introduce a streamlined yet effective model founded on the “You Only Look Once” algorithm to detect photovoltaic panel defects in intricate settings.

PV-YOLO: Lightweight YOLO for Photovoltaic Panel Fault Detection

In this paper, PV-YOLO is proposed to replace YOLOX''s backbone network, CSPDarknet53, with a transformer-based PVTv2 network to obtain local connections between images and feature maps to

Fault Detection and Classification for Photovoltaic Panel System Using

Advances in automation, prediction, and management have enabled sophisticated fault detection methods to enhance system reliability and availability. This paper emphasizes the pivotal

Defect analysis and performance evaluation of photovoltaic modules

EL is a method that applies electrical current to stimulate PV cells to emit light, thereby identifying defects such as cracks and performance degradation. This technique is particularly

YOLO-LitePV: a lightweight detection algorithm for photovoltaic panel

To address the low operational efficiency of detection algorithms and the low accuracy due to the similarity and large-scale variance of PV defects, we propose an improved lightweight

Optimized YOLO based model for photovoltaic defect detection in

In this study, PV-YOLOv12n is introduced as an optimized variant of YOLOv12n, tailored for defect detection in electroluminescence (EL) images of PV panels.

Fault Detection in Solar Energy Systems: A Deep Learning Approach

This study explores the potential of using infrared solar module images for the detection of photovoltaic panel defects through deep learning, which represents a crucial step toward

A photovoltaic panel defect detection framework enhanced by deep

This paper presents a lightweight object detection algorithm based on an improved YOLOv11n, specifically designed for photovoltaic panel defect detection. The goal is to enhance the

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