Detector Solar Cell

Solar cell

A solar cell, also known as a photovoltaic cell (PV cell), is an electronic device that converts the energy of light directly into electricity by means of the photovoltaic effect. [1] It is a form of photoelectric cell, a device whose electrical characteristics (such as current, voltage, or resistance) vary when it is exposed to light. Individual solar cell devices are often the electrical

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An efficient CNN-based detector for photovoltaic module cells

Electroluminescence (EL) imaging provides a high spatial resolution for inspecting photovoltaic (PV) cells, enabling the detection of various types of PV cell defects. Recently, convolutional neural network (CNN) based automatic detection methods for PV cell defects using EL images have attracted much attention. However, existing methods

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Solar Cell Surface Defect Detection Based on Improved YOLO v5

Abstract: A solar cell defect detection method with an improved YOLO v5 algorithm is proposed for the characteristics of the complex solar cell image background, variable defect morphology, and large-scale differences. First, the deformable convolution is incorporated into the CSP module to achieve an adaptive learning scale and perceptual

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Solar panel defect detection design based on YOLO v5 algorithm

Defects of solar panels can easily cause electrical accidents. The YOLO v5 algorithm is improved to make up for the low detection efficiency of the traditional defect detection methods.

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Solar Cells and Photodetectors | SpringerLink

As a result, various photonic devices such as laser diodes (LDs), light-emitting diodes (LEDs), solar cells, and photodetectors using III-V semiconductors have been developed for use in power generation, optical communications, displays and solidstate light sources, data transmission, and signal processing. Depending on the device structures and operating modes, photonic devices

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Accurate detection and intelligent classification of solar cells

In this paper, addressing the challenges of low accuracy in detecting small surface defects on solar cells and limited defect categories, a lightweight solar cell detection model named YOLOPL is proposed. The contributions of this study are as follows: The introduction of YOLOPL, an optimized and improved solar cell defect recognition model

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(PDF) Deep Learning Methods for Solar Fault Detection and

Stoicescu, " Automated Detection of Solar Cell Defects with Deep Learning," in 2018 26th European Signal Processing Conference (EUSIPCO), 2018, pp. 2035–2039.

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A PV cell defect detector combined with transformer and attention

Automated defect detection in electroluminescence (EL) images of

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An efficient and portable solar cell defect detection system

Solar cell defects are a major reason for PV system efficiency degradation, which causes disturbance or interruption of the generated electric current. In this study, a novel system for discovering solar cell defects is proposed, which is compatible with portable and low computational power devices. It is based on K-means, MobileNetV2 and linear discriminant

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A review of automated solar photovoltaic defect detection

In this paper, data analysis methods for solar cell defect detection are categorised into two forms: 1) IBTs, which depend on analysing the deviations of optical properties, thermal patterns, or other visual features in images, and 2) ETTs, which depend on comparing the deviations of the module''s measured electrical parameters from the

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High-Precision Defect Detection in Solar Cells Using YOLOv10

EL imaging is a widely used technique in the photovoltaic industry for identifying defects in solar cells. The process involves applying a forward bias to the solar cell and capturing the emitted infrared light, which reveals defects such as

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Automated visual inspection of solar cell images using adapted

The surface of solar cell products is critically sensitive to existing defects, leading to the loss of efficiency. Finding any defects in the solar cell is a significantly important task in the quality control process. Automated visual inspection systems are widely used for defect detection and reject faulty products. Numerous methods are proposed to deal with defect

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Adaptive automatic solar cell defect detection and classification

Solar cell defect characterization: Generally, the local defects are shown up as dark spots in solar cell EL images, other defect shapes such as micro-crack, large-area failure, break, and finger-interruption are simply regarded as continuous dark spots [20, 21, 51, 53]. However, the recognition of defects in EL images is strongly dependent on the injection

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BAF-Detector: An Efficient CNN-Based Detector for Photovoltaic Cell

Abstract: The multiscale defect detection for photovoltaic (PV) cell electroluminescence (EL) images is a challenging task, due to the feature vanishing as network deepens. To address this problem, an attention-based top-down and bottom-up architecture is developed to accomplish multiscale feature fusion. This architecture, called bidirectional

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Solar Cell Surface Defect Detection Based on Optimized YOLOv5

Abstract: Traditional vision methods for solar cell defect detection have problems such as low accuracy and few types of detection, so this paper proposes an optimized YOLOv5 model for more accurate and comprehensive identification of defects in solar cells. The model firstly integrates five data enhancement methods, namely Mosaic, Mixup, hsv transform, scale

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An efficient CNN-based detector for photovoltaic module cells

Many methods have been proposed for detecting defects in PV cells [9], among which electroluminescence (EL) imaging is a mature non-destructive, non-contact defect detection method for PV modules, which has high resolution and has become the main method for defect detection in PV cells [10].However, manual visual assessment of EL images is time

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Solar Cell Surface Defect Detection Based on Optimized YOLOv5

Traditional vision methods for solar cell defect detection have problems such as low accuracy and few types of detection, so this paper proposes an optimized YOLOv5 model for more accurate and comprehensive identification of defects in solar cells.

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A PV cell defect detector combined with transformer and

Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor-intensive and costly...

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Multi-scale YOLOv5 for solar cell defect detection

CHEN Yafang,LIAO Fei,HUANY Xinyu,et al.Multi-scale YOLOv5 for solar cell defect detection[J].Optics and Precision Engineering,2023,31(12):1804-1815.

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Anomaly Detection and Automatic Labeling for Solar Cell

This model enables the detection and localization of anomalous patterns within the solar cells from the beginning, using only non-defective samples for training and without any manual labeling involved. In a second stage, as defective samples arise, the detected anomalies will be used as automatically generated annotations for the supervised training of a Fully

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An efficient CNN-based detector for photovoltaic module cells

Electroluminescence (EL) imaging provides a high spatial resolution for

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BAF-Detector: An Efﬁcient CNN-Based Detector for Photovoltaic Solar

Solar cell defect detection aims to predict the class and location of multi-scale defects in a electroluminescence (EL) near-infrared image [2], [3], which is captured and processed

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