6 天之前· Experimental results demonstrate that the proposed YOLOv8-AFA algorithm achieves a mean average precision (mAP) of 91.5% in photovoltaic module fault detection tasks, representing a 2.2% improvement over the original YOLOv8 model. Moreover, the generalization capability of the algorithm was rigorously validated on the PASCAL VOC dataset, achieving a …
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 new module includes both standard convolution and dilated convolution, enabling an increase in network depth and receptive field without reducing the output feature map size. This improvement can help to enhance the accuracy of defect detection for photovoltaic modules.
This new module has smaller parameters than the original bottleneck module, which is useful to improve the defect detection speed of the photovoltaic module. Thirdly, a feature interactor is designed in the detection head to enhance feature expression in the classification branch. This helps improve detection accuracy.
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.
To demonstrate the performance of our proposed model, we compared our model with the following methods for PV cell defect detection: (1) CNN, (2) VGG16, (3) MobileNetV2, (4) InceptionV3, (5) DenseNet121 and (6) InceptionResNetV2. The quantitative results are shown in Table 5.
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 .
6 · Experimental results demonstrate that the proposed YOLOv8-AFA algorithm achieves a mean average precision (mAP) of 91.5% in photovoltaic module fault detection tasks, representing a 2.2% improvement over the original YOLOv8 model. Moreover, the generalization capability of the algorithm was rigorously validated on the PASCAL VOC dataset, achieving a …
Get PriceIn 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 assignment, which effectively mitigates the impact of the problem of scant data and data imbalance on model performance; (2) to propose a ...
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Get PriceA novel intelligent end-to-end detection for module defects framework for PV power plants combining the visible and infrared images has been presented for the first time. The field test results show that the PV module has been accurately identified, the identified defect types are accurately classified, and the defective PV module ...
Get PriceIn 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-level annotations indicating whether each PV module contains defective cells, thereby substantially reducing the required annotation costs. The CNN is ...
Get PriceIn 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 …
Get PriceWe propose a novel method for efficient detection of PV cell defects using EL images. We use CLAHE algorithm to improve EL image contrast. We propose GCAM for aiding in distinguishing defects with similar local details. The experimental results show the proposed method is superior to state-of-the-art methods.
Get PriceAutomated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor …
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Get PriceTraditional methods of defect detection in PV cells have often relied on manual inspection, which is time-consuming, subjective, and limited in scalability. In recent years, the convergence of deep learning and imaging technology has opened up new avenues for efficient and accurate defect detection . Specifically, electroluminescence (EL ...
Get PriceIn response to this problem, we introduce the Efficient Long-Range Convolutional Network (ELCN) module, designed to enhance defect detection capabilities in EL images of …
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Get PriceWe propose a photovoltaic cell defect detection model capable of extracting topological knowledge, aggregating local multi-order dynamic contexts, and effectively...
Get PriceIn 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 …
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Get Price6 · Experimental results demonstrate that the proposed YOLOv8-AFA algorithm achieves a mean average precision (mAP) of 91.5% in photovoltaic module fault detection tasks, …
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Get PriceIn response to this problem, we introduce the Efficient Long-Range Convolutional Network (ELCN) module, designed to enhance defect detection capabilities in EL images of PV cells. The ELCN module is based on the ConvNeXt block, renowned for its efficiency and scalability, and integrates the design principles of the Cross-Stage ...
Get PriceAutomated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor-intensive and...
Get PriceZhu, J. et al. C2DEM-YOLO: improved YOLOv8 for defect detection of photovoltaic cell modules in electroluminescence images. Nondestruct Test. Eval 1–23 (2024). Liu, Q. et al. A real-time anchor ...
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Get PriceIn 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 …
Get PriceA novel intelligent end-to-end detection for module defects framework for PV power plants combining the visible and infrared images has been presented for the first time. …
Get PriceWe propose a photovoltaic cell defect detection model capable of extracting topological knowledge, aggregating local multi-order dynamic contexts, and effectively...
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Get PriceDetection Technology Based on YOLOv5 Shangxian Teng, Zhonghua Liu(B), Yichen Luo, ... scale, the SE attention module and GhostBottleneck are introduced on the basis of the YOLOv5 model, which accelerates the extraction of useless features and enhances the precision of small object perception by capturing the features of defects of different scales and fusing semantic …
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