M. Y. Demirci, N. Beşli, A. (2019) Gümüşçü, Defective PV cell detection using deep transfer learning and EL imaging, Int Conf Data Sci, Mach Learn and Stat 2019 (DMS-2019) 2019. Google Scholar M. W. Akram et al (2019) CNN based automatic detection of photovoltaic cell defects in electroluminescence images. Energy 189.
A hybrid fuzzy convolutional neural network based mechanism for photovoltaic cell defect detection with electroluminescence images IEEE Transactions on Parallel and Distributed Systems, 32 ( 7 ) ( 2021 ), pp. 1653 - 1664, 10.1109/TPDS.2020.3046018
Akram MW, Li G, Jin Y et al (2019) CNN based automatic detection of photovoltaic cell defects in electroluminescence images[J]. Energy 189:116319. Article Google Scholar Pierdicca R, Paolanti M, Felicetti A et al (2020) Automatic faults detection of photovoltaic farms: solAIr, a deep learning-based system for thermal images[J]. Energies …
To detect defects on the surface of PV cells, researchers have proposed methods such as electrical characterization [], electroluminescence imaging [7,8,9], infrared (IR) imaging [], etc. EL imaging is frequently utilized in solar cell surface detection studies because it is rapid, non-destructive, simpler and more practical to integrate into actual manufacturing …
With the proposed goal of "Carbon Neutrality", photovoltaic energy is gradually gaining the leading role in energy transformation. At present, crystalline silicon cells are still the mainstream technology in the photovoltaic industry, but due to the similarity of defect characteristics and the small scale of the defects, automatic defect detection of photovoltaic …
With the global increase of photovoltaic (PV) modules deployment in recent years, the need to explore and realize their reported failure mechanisms has become crucial.
Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor-intensive and...
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000. Digital Object Identifier 10.1109/ACCESS.2023.0322000 Deep Learning for Automatic Defect Detection in
To enhance detection efficiency, several automatic detection methods have been proposed. For instance, Sergiu Deitsch et al. [14] proposed a robust automatic image segmentation technique, which utilized straight line features of busbars. Then, the segmented PV panels were classified and detected using support vector machines (SVM) and an end-to ...
This work introduces neural architecture search to the field of PV cell defect classification for the first time and proposes a novel lightweight high-performance model for …
energy, interesting solutions are represented by photovoltaic (PV) cells, wind generators, biomass plants and fuel cells. In particular, photovoltaic systems can be considered one of the most widespread solution with significant margins of improvement while ensuring the generation of energy with low environmental impact.
Download Citation | An automatic detection model for cracks in photovoltaic cells based on electroluminescence imaging using improved YOLOv7 | The increasing interest in photovoltaic (PV) energy ...
Different statistical outcomes have affirmed the significance of Photovoltaic (PV) systems and grid-connected PV plants worldwide. Surprisingly, the global cumulative installed capacity of solar PV systems has massively increased since 2000 to 1,177 GW by the end of 2022 [1].Moreover, installing PV plants has led to the exponential growth of solar cell …
It is a novel micro-crack detection model for automated pixel-level micro-crack detection of PV module cells. The M-shaped structure solves "All Black" issue that is easy to occur due to the severe imbalance of the micro-crack segmentation dataset. And integration of attention mechanism into the network significantly improves the accuracy of segmentation. Because of …
Photovoltaic (PV) cell defect detection has become a prominent problem in the development of the PV industry; however, the entire industry lacks effective technical means. In this paper, we propose a deep-learning-based defect detection method for photovoltaic cells, which addresses two technical challenges: (1) to propose a method for data enhancement and category weight …
An automatic detection model for cracks in photovoltaic cells based on electroluminescence imaging using improved YOLOv7. Original Paper. Published: 10 October 2023. Volume 18, pages 625–635, (2024) Cite …
The numerical experimental results show that the proposed deep-learning-based defect detection method for PV cells can automatically perform efficient and accurate defect detection using EL images. Photovoltaic (PV) cell defect detection has become a prominent problem in the development of the PV industry; however, the entire industry lacks …
The past two decades have seen an increase in the deployment of photovoltaic installations as nations around the world try to play their part in dampening the impacts of global warming. The manufacturing of solar cells can be defined as a rigorous process starting with silicon extraction. The increase in demand has multiple implications for manual quality …
A photovoltaic cell defect polarization imaging small target detection method based on improved YOLOv7 is proposed to address the problem of low detection accuracy caused by insufficient feature extraction ability in the process of small target defect detection. Firstly, polarization imaging technology is introduced, using polarization degree images as …
Deep learning methods have steadily been applied to industrial defect detection studies in recent years, and many scholars have studied the automatic detection of PV cell defects based on EL imaging methods. Deitsch et al. [22] proposed two deep-learning-based methods for the automatic detection of PV cell defects with convolutional neural
To solve these problems, we propose a novel lightweight high-performance model for automatic defect detection of PV cells in electroluminescence(EL) images based …
A novel Bidirectional Attention Feature Pyramid Network (BAFPN) is designed by combining the novel multi-head cosine non-local attention module with top-down and bottom-up feature pyramid networks through bidirectional cross-scale connections, which can make all layers of the pyramid share similar semantic features. The multi-scale defect detection for …
Convolutional neural networks (CNNs) have become a prominent tool in the automatic detection of surface defects in photovoltaic (PV) cells. Leveraging extensive …
In this paper, we propose a deep-learning-based defect detection method for photovoltaic cells, which addresses two technical challenges: (1) to propose a method for data enhancement and category …
DOI: 10.1007/s11760-023-02724-7 Corpus ID: 264047989; An automatic detection model for cracks in photovoltaic cells based on electroluminescence imaging using improved YOLOv7
BAF-Detector: An Efficient CNN-Based Detector for Photovoltaic Cell Defect Detection IEEE Transactions on Industrial Electronics 10.1109/tie.2021.3070507
The anomaly detection in photovoltaic (PV) cell electroluminescence (EL) image is of great significance for the vision-based fault diagnosis. Many researchers are committed to solving this problem ...
As a competitive renewable electricity generation technology, solar photovoltaic (PV) generation expands very quickly and its consumption doubles from 4 % of overall renewable energy consumption in 2017 to approximately 8 % in 2023 [1].The PV panel, which comprises multiple cells connected in series and parallel, serves as the fundamental …
Electroluminescence (EL) imaging provides a high spatial resolution for inspecting photovoltaic (PV) cells, enabling the detection of various types of PV cell defects. Recently, …
DOI: 10.1016/j.solener.2020.03.049 Corpus ID: 216462788; Deep learning based automatic defect identification of photovoltaic module using electroluminescence images @article{Tang2020DeepLB, title={Deep learning based automatic defect identification of photovoltaic module using electroluminescence images}, author={Wuqin Tang and Qiang …
classification and detection results in raw solar cell EL images. Index Terms—photovoltaic solar cell, multi-scale defect detection, deep learning, cosine non-local attention, feature pyramid network I. INTRODUCTION T HE multicrystalline solar cell defects will lead to a seri-ously negative impact on the power generation efficiency.
This paper presents a deep-learning-based automatic detection model SeMaCNN for classification and anomaly detection of electroluminescent images for solar cell quality evaluation. The core of the ...
This paper develops an automatic defect detection mechanism using texture feature analysis and supervised machine learning method to classify the failures in photovoltaic (PV) modules.
automatic defect detection of PV cells. However, the parameters of these CNN-based models are very large, which require stringent hardware resources and it is di cult to be applied in actual industrial projects. To solve these problems, we propose a novel lightweight high-performance model for automatic defect detection of PV cells in …
Attention M-net is a novel micro-crack detection model for automated pixel-level micro-Crack detection of PV module cells in EL images which solves "All Black" issue that is easy to occur and integration of attention mechanism into the network significantly improves the accuracy of segmentation. In the solar power industry, quality inspection of solar cells is a …
In the intelligent manufacturing process of solar photovoltaic (PV) cells, the automatic defect detection system using the Industrial Internet of Things (IIoT) smart cameras and sensors cooperated in IIoT has become a promising solution. Many works have been devoted to defect detection of PV cells in a data-driven way. However, because of the subjectivity and …
Convolutional neural networks (CNNs) have become a prominent tool in the automatic detection of surface defects in photovoltaic (PV) cells. Leveraging extensive datasets of PV cell images, CNNs ...
CNN based automatic detection of photovoltaic cell defects in electroluminescence images. Energy, 189 (2019), Article 116319. View PDF View article View in Scopus Google Scholar. Alec et al., 2015. R. Alec, M. Luke, C. Soumith. Unsupervised representation learning with deep convolutional generative adversarial networks. Comput. Sci. …
Anomaly Detection Algorithm for Photovoltaic Cells Based on Lightweight Multi-Channel Spatial Attention Mechanism
for Photovoltaic Cell Defect Detection Binyi Su, Haiyong Chen, and Zhong Zhou, Member, IEEE Abstract—The multi-scale defect detection for photo-voltaic (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 accom-plish …
From a high-level perspective, while IBTs provide a high-resolution visual representation of the module surface, allowing for the detection and diagnosis of small …
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 …
