Various defects in PV cells can lead to lower photovoltaic conversion efficiency and reduced service life and can even short circuit boards, which pose safety hazard risks . As a result, PV cell defect detection research offers a crucial assurance for raising the caliber of PV products while lowering production costs. Figure 1.
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.
Many methods have been proposed for detecting defects in PV cells , 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 .
Recently, convolutional neural network (CNN) based automatic detection methods for PV cell defects using EL images have attracted much attention. However, existing methods struggle to achieve a good balance between detection accuracy and efficiency. To address this issue, we propose a novel method for efficient PV cell defect detection.
Before the emergence of deep learning techniques, various traditional methods were employed for anomaly detection in photovoltaic (PV) cells. These methods can be broadly categorized into two groups: statistical analysis, and signal processing.
Visualizing feature map (The figure illustrates the change in the feature map after the SRE module.) We propose a photovoltaic cell defect detection model capable of extracting topological knowledge, aggregating local multi-order dynamic contexts, and effectively capturing diverse defect features, particularly for small flaws.
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 …
Anomaly detection in photovoltaic (PV) cells is crucial for ensuring the efficient operation of solar power systems and preventing potential energy losses. In this paper, we …
A dataset has been created for detecting anomalies in photovoltaic cells on a large scale in [], this dataset consists of 10 categories, several detection models were …
We propose a photovoltaic cell defect detection model capable of extracting topological knowledge, aggregating local multi-order dynamic contexts, and effectively …
Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor …
Photovoltaic cell defect detection. Contribute to binyisu/PVEL-AD development by creating an account on GitHub.
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 …
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, …
Abstract: The multi-scale defect detection for photovoltaic (PV) cell electroluminescence (EL) images is a challenging task, due to the feature vanishing as …
Abstract: The multi-scale defect detection for photovoltaic (PV) cell electroluminescence (EL) images is a challenging task, due to the feature vanishing as …
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 …
An evaluation of the proposed YOLOv7 model''s ability to detect in PV cell cracks was conducted by comparing it with popular YOLO models. The improved YOLOv7 model achieves 88.03% of precision, 74.97% …
Photovoltaic cell defect detection. Solar cell EL image defect detection dataset News [2023-12-19]：2！Reply within 2 weeks!I am busy with my graduation thesis, please …
This paper reviews all analysis methods of imaging-based and electrical testing techniques for solar cell defect detection in PV systems. This section introduces a comparative …
In this study, we propose a weakly supervised learning method to build a CNN for cell-level defect detection in a cost-efficient manner. Our method uses a training dataset solely with module …
We propose a photovoltaic cell defect detection model capable of extracting topological knowledge, aggregating local multi-order dynamic contexts, and effectively …
Electroluminescence (EL) imaging provides a high spatial resolution for inspecting photovoltaic (PV) cells, enabling the detection of various types of PV cell defects. …
To solve these problems, we propose a novel lightweight high-performance model for automatic defect detection of PV cells in electroluminescence(EL) images based on …
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, …
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 …
Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor …
Artificial intelligence has been the subject of research, particularly computer vision, to eliminate the drawbacks associated with human inspection and boost solar cell production output efficiency. Convolutional …
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 …
This paper reviews all analysis methods of imaging-based and electrical testing techniques for solar cell defect detection in PV systems. This section introduces a comparative …
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 …