DOI: 10.12677/MET.2013.24023 Corpus ID: 113027068; Halcon Surface Defects Inspection of Solar Cell Based on Halcon @article{2013HalconSD, title={Halcon Surface Defects Inspection of Solar Cell Based on Halcon}, author={ and and and and }, journal={Metrologia}, year={2013}, …
facilitate continuous, fast, and accurate solar PV plant quality inspection. Proper maintenance of solar PV panels improves their efficiency and power output. Keywords: photovoltaic, electroluminescence imaging, solar PV inspection, solar cell defects, deep learning, solar cell classification 1. Introduction
DOI: 10.3788/fgxb20133408.1028 Corpus ID: 138983296; Defect detection of solar cells based on electroluminescence imaging @article{Chen2013DefectDO, title={Defect detection of solar cells based on electroluminescence imaging}, author={Wen-Zhi Chen and Feng-Yan Zhang and Ran Zhang and Chao Li and }, journal={Chinese Journal of Luminescence}, …
The photovoltaic (PV) system industry is continuously developing around the world due to the high energy demand, even though the primary current energy source is fossil fuels, which are a limited source and other sources are very expensive. Solar cell defects are a major reason for PV system efficiency degradation, which causes disturbance or interruption …
Chen et al. 19 developed a novel solar CNN architecture to classify defects in visible light images of solar cells. Han et al. 20 proposed a deep learning-based defect …
Download Citation | On Jan 1, 2013, published Surface Defects Inspection of Solar Cell Based on Halcon | Find, read and cite all the research you need on ResearchGate
Fluorescent magnetic particle inspection (MPI) is a conventional non-destructive testing process for railway bearing rings that still needs to be completed manually. ... and CIOU loss function to enhance the model performance and shows that the improved YOLO v5 algorithm can complete the solar cell defect detection task more accurately while ...
This section reviews the existing work on solar cell defect detection, focusing on both conventional methods and recent advancements in deep learning-based approaches. ... Solar cell surface defect inspection based on multispectral convolutional neural network. Journal of Intelligent Manufacturing, 31 (2) (2020), pp. 453-468.
This paper uses Mosaic and MixUp fusion data enhancement, K-meansCC clustering anchor box algorithm, and CIOU loss function to enhance the model performance and shows that the improved YOLO v5 algorithm can complete the solar cell defect detection task more accurately while meeting the real-time requirements. A solar cell defect detection method with an …
Drone-Based Solar Cell Inspection With Autonomous Deep Learning. Zhounan Wang, Zhounan Wang. Aerial Robotics Laboratory, Imperial College London, London, United Kingdom ... There are two main phases for this framework: detection of the solar panel location and identification of the solar cell defect with a feasible set of trajectories.
Automatic crack defect detection for multicrystalline solar cells is a challenging task, owing to inhomogeneously textured background, disturbance of crystal grains pseudo defects, and low ...
Semantic Scholar extracted view of "Investigation of visual inspection method for silicon solar cell: Investigation of visual inspection method for silicon solar cell" by Wu-Jie Zhang et al. ... Simulation results show that the MCCNN can quickly and accurately identify the PV module cells defect and defect categories, and accurately mark it on ...
An in-line, non-destructive process for characterizing polycrystalline thin-film and other large area electronic devices using computer vision based imaging of the manufacturing and inspection steps during the device fabrication process are providing new insights into the causes of poor performance in CdTe-based solar cells. Expand
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, …
The state-of-the-art methods of solar cell surface defects detection based on computer vision, classified into three categories: local scheme, global scheme and local-global scheme based methods, are reviewed. Various types of defects exist in the solar cell surface because of some uncontrollable factors during the process of production. The solar cell …
Solar cells or photovoltaic systems have been extensively used to convert renewable solar energy to generate electricity, and the quality of solar cells is crucial in the electricity-generating process. Mechanical defects such as cracks and pinholes affect the quality and productivity of solar cells. Thus, it is necessary to detect these defects and reject the …
A bidirectional strip refinement attention (BSRA) adaptively capturing long-range spatial dependency with direction information and long-range channel dependency is proposed, outperforming existing state-of-the-art methods. High-performance defect segmentation techniques are essential for the high-quality manufacturing of polycrystalline solar cells. Edge …
Manual inspection of solar cells is not very effective, so automatic surface defect detection techniques using machine vision are implemented to improve the production quality and also the lifetime and efficiency of solar cells. ... Chen H et al (2020) Solar cell surface defect inspection based on multispectral convolutional neural network. J ...
1 / 14 Haiyong Chen1,2, Yue Pang1, Qidi Hu1, Kun Liu1,2 Abstract Similar and indeterminate defect detection of solar cell surface with heterogeneous texture and complex back-ground is a challenge ...
The study introduces an automated visual inspection system utilizing mathematical morphology and edge-based region analysis to efficiently detect defects in solar …
The early detection of defects as cracks, micro-cracks, and finger failures in solar cells is important for the production of PV modules. Analyzing EL images to locate and identify these failures ...
Different cell defects have different consequences, e.g. dark area leads to an immediate reduced power output while a crack can cause a reduced power output in the future. For this reason, many operators of solar parks wish an automated detection of defect cells and a further classification of defect cells into various defect categories in
The monocrystalline solar cell (MSC) interior is prone to miscellaneous defects that affect energy conversion efficiency and even cause fatal damage to the photovoltaic module. In this study, an automatic defect inspection method for MSC interior is presented. Electroluminescence (EL) imaging technology is utilized to visualize defects inside MSC. Also, accurate cell positioning is …
ABSTRACT Solar cells defects inspection plays an important role to ensure the efficiency and lifespan of photovoltaic modules. However, it is still an arduous task because of the diverse ...
DOI: 10.1109/BEPRL.2004.1308153 Corpus ID: 43759965; Solar cell crack inspection by image processing @article{Fu2004SolarCC, title={Solar cell crack inspection by image processing}, author={Zhuang Fu and Yanzheng Zhao and Liu Yang and Cao Qixin and Mingbo Chen and Zhang Jun and J. Lee}, journal={Proceedings of 2004 International …
Automatic visual inspection techniques for micro-cracks in solar wafers and solar cells are also reviewed by Israil et al [11] and Abdelhamid et al [12]. The currently available machine vision ...
Experimental results indicate that the proposed method can meet the requirements for effectiveness and real-time processing and presents promising results compared to other existing algorithms. 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 …
Seven solar cell states can be detected including breaks, finger interruptions, material defects, and microcracks. This pipeline is demonstrated virtually on the NEST building …
Machine Vision for Solar Cell Inspection Dr. Michael G. Mauk, Drexel University ... Engineering at Georgia Institute of Technology. His educational background is in manufacturing with an emphasis on mechatronics. In addition to his many years of industrial experience, he has taught many ... including grain boundaries and defects. As part of a ...
This paper presents a deep learning-based automatic detection of multitype defects to fulfill inspection requirements of production line. At first, a database composed of …
The widespread adoption of solar energy as a sustainable power source hinges on the efficiency and reliability of photovoltaic (PV) cells. These cells, responsible for the conversion of sunlight into electricity, are subject to various internal and external factors that can compromise their performance [] fects within PV cells, ranging from micro-cracks to material …
To improve the adaptability to the scale changes of various types of surface defects of solar cells, this study proposed a multi-feature region proposal fusion network (MF-RPN) structure to detect ...
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 …
We propose a photovoltaic cell defect detection model capable of extracting topological knowledge, aggregating local multi-order dynamic contexts, and effectively...
Nowadays, renewable energies play an important role to cover the increasing power demand in accordance with environment protection. Solar energy, produced by large solar farms, is a fast …
A new precise and accurate defect inspection method for photovoltaic electroluminescence (EL) images and a hybrid loss which combines focal loss and dice loss aiming to solve two problems: a) overcome the class imbalance problem, and b) allowing the network to train with irregular image labels for some complex defects. Solar cells defects …
However, the model accuracy still needs to be improved. Chiou et al. developed a model for extracting crack defects in solar cell images using a regional growth detection algorithm. The authors of used the machine vision …
For the recognition of texture variations in the scanning electron microscope images caused by these defects is crucial the definition of a set of features for texture representation, and the results show effectiveness of the proposed methodology. Physical defects reduce the organic solar cells (OSC) functioning. Throughout the OSC fabrication process, the …
DOI: 10.1016/J.SOLMAT.2011.12.007 Corpus ID: 97806427; Defect detection of solar cells in electroluminescence images using Fourier image reconstruction @article{Tsai2012DefectDO, title={Defect detection of solar cells in electroluminescence images using Fourier image reconstruction}, author={Du-ming Tsai and Shih-Chieh Wu and Wei-Chen Li}, journal={Solar …
This paper presents defect inspection of multicrystalline solar cells in electroluminescence (EL) images. A solar cell charged with electrical current emits infrared …
DOI: 10.1016/j frared.2020.103334 Corpus ID: 218968562; Detection of surface defects on solar cells by fusing Multi-channel convolution neural networks @article{Zhang2020DetectionOS, title={Detection of surface defects on solar cells by fusing Multi-channel convolution neural networks}, author={Xiong Zhang and Yawen Hao and Hong Shangguan and Pengcheng …
about train ing and inspection of solar cell surface defect mainly include ： 1) There are 6 types of defects in the dataset. The characteristics of each defect type are quite different in
Solar cell inspection via photoluminescence imaging in the NIR/SWIR Introduction . The use of photoluminescence (PL) imaging to inspect solar cells is a rapidly growing area of ... defects throughout the entire mc-Si solar cell manufacturing process. This series of wafer images, for instance,was acquired at the following stages: (1) as-cut, (2 ...
The proposed adaptive automatic solar cell defect detection and classification method mainly consists of the following three steps: solar cell EL image preprocessing, …
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