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With the deepening of intelligent technology, deep learning detection algorithm can more accurately and easily identify whether the solar panel is defective and the specific defect category, which is broadly divided into two-stage detection algorithm and one-stage detection algorithm.
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 algorithms to cluster solar cell images and develop a detection model for each constructed cluster.
In order to avoid such accidents, it is a top priority to carry out relevant quality inspection before the solar panels leave the factory. For the defect detection of solar panels, the main traditional methods are divided into artificial physical method and machine vision method.
The VarifocalNet is an anchor-free detection method and has higher detection accuracy 5. To further improve both the detection accuracy and speed for detecting photovoltaic module defects, a detection method of photovoltaic module defects in EL images with faster detection speed and higher accuracy is proposed based on VarifocalNet.
The detection of defects in photovoltaic models can be categorized into two types. The first type involves analyzing the characteristic curves of electrical parameters, such as current, voltage, and power of the photovoltaic system.
The deep learning method also has been widely used in photovoltaic module defect detection 10. To reduce the detection network complexity, Akram et al. 11 proposed a light convolution neural network based on a visual geometry group network for detecting photovoltaic cell cracking defects.
Traditionally, defect detection in EL images of PV cells has relied on labor-intensive manual inspection, which are not only time-consuming but also prone to human …
Here each heatmap shows the quantity of defects observed in each cell in a 16 × 8 solar module. Defects are recognized by YOLO model. The right-hand side of the image is …
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, …
To improve the detection accuracy of photovoltaic module defects based on VarifocalNet, we use the new bottleneck module to replace the first bottleneck module used in the last stage convolution ...
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, …
To improve the detection accuracy of photovoltaic module defects based on VarifocalNet, we use the new bottleneck module to replace the first bottleneck module used in …
Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor …
Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor …
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 …
This study presents an advanced defect detection approach for solar cells using the YOLOv10 deep learning model. Leveraging a comprehensive dataset of 10,500 solar cell …
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 …
Wang et al. presented a data augmentation method and category weight assignment model for PV cell defect detection through using channel attention and ResNet152 …
First, the deformable convolution is incorporated into the CSP module to achieve an adaptive learning scale and perceptual field size; then, the feature extraction capability of the model is …
Methods for defect detection and classification in EL images include: statistical methods for pixel-level crack detection [16], Random Forests (RFs) and SVMs for detection 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 …
LBIC can potentially yield comprehensive diagnoses for structural and process-based solar cell defects. Unlike EBIC, this method flows photogenerated current in solar cells …
Scientific Reports - Defect detection of photovoltaic modules based on improved VarifocalNet. ... L. Solar cell surface defect detection based on improved YOLO v5. IEEE Access 10, 80804–80815.
Automated analysis and defect detection of PV module level EL images are critical to derive useful information from batches of PV modules bought and sold throughout …