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An efficient and portable solar cell defect detection system
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 …
CNN based automatic detection of photovoltaic cell defects in electroluminescence images …
CNN based automatic detection of photovoltaic cell defects in electroluminescence images Author links open overlay panel M. Waqar Akram a d, Guiqiang Li b, Yi Jin a, Xiao Chen c, Changan Zhu a, Xudong Zhao b, Abdul Khaliq d, M. Faheem d, Ashfaq Ahmad a ...
Dynamic Domain Adaptation Object Detection for Photovoltaic Cell Defects
The task of defect detection of photovoltaic (PV) cell electroluminescence (EL) is an important part of its manufacturing process. There are differences in background, defect contrast and resolution (Domain Shift) during the quality inspection of photovoltaic cell due to intrinsic factors in machine. Classical object detection methods fail to perform as …
Failures of Photovoltaic modules and their Detection: A Review
The temperature variation among different cells/cell regions due to presence of defects (if any) can be detected from infrared images taken by thermal imagers [103], [106]. The indoor and outdoor thermography setups are shown in …
A lightweight network for photovoltaic cell defect detection in …
Nowadays, the rapid development of photovoltaic(PV) power stations requires increasingly reliable maintenance and fault diagnosis of PV modules in the field. Due to the effectiveness, convolutional neural network (CNN) has been widely used in the existing automatic defect detection of PV cells. However, the parameters of these CNN …
An efficient CNN-based detector for photovoltaic module cells defect detection …
To further improve detection performance of CNN-based PV cell defect detection method, in this paper, we propose a novel, efficient method for PV cell defect detection using EL images. Specifically, in data preprocessing phase, to reduce the effect of low contrast EL images on detection result, we utilize Contrast Limited Adaptive …
Deep learning based automatic defect identification of …
This paper presented a deep learning-based defect detection of PV modules using electroluminescence images through addressing two technical challenges: …
Photovoltaic Cell Defect Detection Model based-on Extracted …
DOI: 10.1109/ICAIIC48513.2020.9065065 Corpus ID: 215816518 Photovoltaic Cell Defect Detection Model based-on Extracted Electroluminescence Images using SVM Classifier @article{SerfaJuan2020PhotovoltaicCD, title={Photovoltaic Cell Defect Detection Model based-on Extracted Electroluminescence Images using SVM Classifier}, author={Ronnie …
Photovoltaic cell defect classification using …
The authors of [] presented an automatic defect detection approach to classifying the saw-mark defects in multi-crystalline solar wafer images. In their study, they utilised CNN model for classification. Chen et …
An Efficient Yolox-Based Method for Photovoltaic Cell Defect Detection
Therefore, this paper proposes a high-efficiency photovoltaic cell defect detection method based on improved YOLOX. First, the transfer learning strategy is adopted to accelerate model convergence. Secondly, to suppress the interference of complex backgrounds, the SENet attention mechanism is added to the feature extraction …
[2012.10631] BAF-Detector: An Efficient CNN-Based Detector for …
The multi-scale defect detection for photovoltaic (PV) cell electroluminescence (EL) images is a challenging task, due to the feature vanishing as …
Review A review of automated solar photovoltaic defect detection …
Camera enhanced compressive light beam induced current sensing for efficient defect detection in photovoltaic cells
A lightweight network for photovoltaic cell defect detection in …
View a PDF of the paper titled A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation, by Jinxia Zhang and 3 other authors View PDF Abstract: Nowadays, the rapid development of photovoltaic(PV) power stations requires increasingly reliable …
Adaptive automatic solar cell defect detection and classification based on absolute electroluminescence imaging …
Current defect inspection methods for photovoltaic (PV) devices based on electroluminescence (EL) imaging technology lack juggling both labor-saving and in-depth understanding of defects, restricting the progress towards yield improvement and higher efficiency.
BAF-Detector: An Efficient CNN-Based Detector for Photovoltaic Cell Defect Detection …
The multi-scale 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 multi-scale feature fusion.
PD-DETR: towards efficient parallel hybrid matching with transformer for photovoltaic cell defects detection
Defect detection for photovoltaic (PV) cell images is a challenging task due to the small size of the defect features and the complexity of the background characteristics. Modern detectors rely mostly on proxy learning objectives for prediction and on manual post-processing components. One-to-one set matching is a critical design for …
Deep Learning-Based Defect Detection for Photovoltaic Cells …
This paper focuses on defect detection in photovoltaic cells using the innovative application of deep learning techniques. Through extensive exploration and experimentation with a variety of deep learning models, we have gained valuable insights into the potential of these models to accurately classify PV cells as either defective or …
Photovoltaic Cell Defect Detection by Lock-In Thermography …
In order to detect defective photovoltaic cells, several monitoring techniques, such as lock-in thermography, have been widely used alongside some …
Defect detection and quantification in electroluminescence images of …
Cnn based automatic detection of photovoltaic cell defects in electroluminescence images Energy, 189 (2019), p. 116319 View PDF View article View in Scopus Google Scholar [18] S. Deitsch, V. Christlein, S. …
Photovoltaic Cell Defect Detection Based on Weakly Supervised …
Abstract: Recently, convolutional neural networks (CNNs) have proven successful in automating the detection of defective photovoltaic (PV) cells within PV modules. …
BAF-Detector: An Efficient CNN-Based Detector for Photovoltaic Cell Defect Detection …
multi-scale defects classification and detection results in raw PV cell EL images. Index Terms—photovoltaic cell, multi-scale defect de-tection, deep learning, cosine non-local attention, feature pyramid network I. INTRODUCTION T HE multicrystalline lead to a
Automated Defect Detection and Localization in Photovoltaic Cells …
A deep learning based semantic segmentation model that identifies and segments defects in electroluminescence images of silicon photovoltaic (PV) cells that can differentiate between cracks, contact interruptions, cell interconnect failures, and contact corrosion for both multicrystalline and monocrystalline silicon cells is proposed. In this …
Sensors | Free Full-Text | Deep-Learning-Based Automatic Detection of Photovoltaic Cell Defects …
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 …
PVEL-AD: A Large-Scale Open-World Dataset for Photovoltaic Cell Anomaly Detection …
Abstract: 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, but a large-scale open-world dataset is …
A lightweight network for photovoltaic cell defect detection in …
A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation Jinxia Zhanga,b,, Xinyi Chen a, Haikun Wei, Kanjian Zhang aKey Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing, 210096, …
A Hybrid Fuzzy Convolutional Neural Network Based Mechanism for Photovoltaic Cell Defect Detection With Electroluminescence Images
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 …
GCSC-Detector: A Detector for Photovoltaic Cell Defect Based on …
A Global Channel and Spatial Context Module (GCSC), which includes the channel and the spatial self-attention module, to adaptively capture the global rich context information, and establish the relationship between each channel and to improve the detection ability for small and weak defects. Due to the existence of many small and …
C2DEM-YOLO: improved YOLOv8 for defect detection of …
Electroluminescence (EL) testing is a method used to detect defects during the production process of these modules. To address the issue of low defect …
CNN based automatic detection of photovoltaic cell defects in …
We presented a novel approach using a light Convolutional Neural Network (CNN) architecture for automatic detection of photovoltaic cell defects in …
An automatic detection model for cracks in photovoltaic cells …
2.1 PV cell image dataset and augmentationThe basic principle behind a PV cell is the PV effect, which occurs when photons of light strike the surface of a semiconductor material. These photons excite electrons within the material, causing them to …
Automated visual inspection of solar cell images using adapted morphological and edge detection …
In literature, various amounts of inspection methods have been presented based on machine vision systems to inspect the solar cell. As mentioned previously, the main focus of this research is the application of AVIS in the solar cell industry. Meng et al. [] proposed a YOLO-based defect object detection algorithm for photovoltaic modules …
BAF-Detector: An Efficient CNN-Based Detector for Photovoltaic …
PV cell defect detection aims to predict the class and location of multi-scale defects in an electroluminescence (EL) near-infrared image [2], [3]. It is captured and processed by the …
A lightweight network for photovoltaic cell defect detection in …
Nowadays, the rapid development of photovoltaic(PV) power stations requires increasingly reliable maintenance and fault diagnosis of PV modules in the field. Due to the effectiveness, convolutional neural network (CNN) has been widely used in the existing automatic defect detection of PV cells..
[2012.10631] BAF-Detector: An Efficient CNN-Based Detector for Photovoltaic Cell Defect Detection …
The multi-scale 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 multi-scale feature fusion. This architecture, …
A photovoltaic surface defect detection method for building based …
A hybrid fuzzy convolutional neural network based mechanism for photovoltaic cell defect detection with electroluminescence images IEEE Trans. Parallel Distrib. Syst., PP (99) (2020), p. 1 View PDF View article Google Scholar [21] Zhang B., …
CNN based automatic detection of photovoltaic cell defects in electroluminescence images …
A framework using CNN is proposed for automatic detection of defects in PV cells. • It achieved state of the art results of 93.02% accuracy on EL image dataset. • It can work on ordinary CPU computer while maintaining real time speed (8.07 ms). • Data
Photovoltaics Cell Anomaly Detection Using Deep Learning …
Photovoltaic cells play a crucial role in converting sunlight into electrical energy. However, defects can occur during the manufacturing process, negatively impacting these cells’ efficiency and overall performance. …