Request PDF | 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 ...
PV cell defect detection aims to predict the class and location of multi-scale defects in an electroluminescence (EL) near-infrared image , . It is captured and processed by the following defect detection system, which integrates various sensors such as leakage circuit breaker to achieve safe and efficient fault elimination of PV cells.
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.
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.
Abstract: 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, an attention-based top-down and bottom-up architecture is developed to accomplish multiscale feature fusion.
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.
However, traditional object detection models prove inadequate for handling photovoltaic cell electroluminescence (EL) images, which are characterized by high levels of noise. To address this challenge, we developed an advanced defect detection model specifically designed for photovoltaic cells, which integrates topological knowledge extraction.
Request PDF | 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 ...
detection based on computer vision plays a vital role in the manufacturing process of PV cells. This process can advantage safe and high-efficiency operation of the large-scale PV farms. …
Photovoltaic (PV) cell modules are the core components of PV power generation systems, and defects in these modules can significantly affect photovoltaic conversion efficiency and lifespan. Electroluminescence (EL) testing is a method used to detect defects during the production process of these modules. To address the issue of low ...
Then embed this module into the YOLOv7 model to form our Global Channel and Spatial Context Detector (GCSC-Detector) to improve the detection ability for small and weak defects. The …
Download Citation | On Jan 1, 2024, Qing Liu and others published An efficient CNN-based detector for photovoltaic module cells defect detection in electroluminescence images | Find, read and cite ...
We 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.
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 this article
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, …
Photovoltaic modules were first mass-produced in 2000, ... photovoltaic cells are simpler and cheaper than mono-si, however tend to make less efficient cells, an average of 13.2%. [66] EPBT ranges from 1.5 to 2.6 years. [67] The cradle to gate of CO 2-eq/kWh ranges from 28.5 to 69 grams when installed in Southern Europe. [68] Assuming that the following countries had a …
Photovoltaic (PV) cell modules are the core components of PV power generation systems, and defects in these modules can significantly affect photovoltaic conversion efficiency and lifespan. Electroluminescence (EL) …
Therefore, it is crucial to identify a set of defect detection approaches for predictive maintenance and condition monitoring of PV modules. This paper presents a …
Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor-intensive and...
Many current deep learning-based methods for detecting defects in photovoltaic modules focus solely on either detection speed or accuracy, which limits their practical application. To...
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, called Bidirectional Attention Feature Pyramid …
Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor …
Recently, convolutional neural networks (CNNs) have proven successful in automating the detection of defective photovoltaic (PV) cells within PV modules. Existing studies have built a CNN based on fully supervised learning, which requires a training dataset consisting of PV cell images annotated according to whether the individual cells are defective. However, manually …
BAF-Detector: An Efficient CNN-Based Detector 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 ...
Currently, defect detection for photovoltaic (PV) electroluminescence (EL) images faces three challenges: limited training data and complex backgrounds result in low accuracy in detecting defects; the diverse shapes of specific defects often lead to frequent false alarms; and existing models still require improvement in accurately recognizing these 12 specific defects. An …
Therefore, it is crucial to identify a set of defect detection approaches for predictive maintenance and condition monitoring of PV modules. This paper presents a comprehensive review of different data analysis methods for defect detection of PV systems with a high categorisation granularity in terms of types and approaches for each technique.
Abstract: 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, an attention-based top-down and bottom-up architecture is developed to accomplish multiscale feature fusion. This architecture, called bidirectional ...
Abstract: 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 …
Then embed this module into the YOLOv7 model to form our Global Channel and Spatial Context Detector (GCSC-Detector) to improve the detection ability for small and weak defects. The experimental results show that the mAP50 of this method reaches 84.8% on the large-scale photovoltaic EL dataset PVEL-AD and it is superior to other methods.
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 …
This work introduces the Efficient Long-Range Convolutional Network (ELCN) module, designed to enhance defect detection capabilities in EL images of PV cells, and proposes a two-stage fine-tuning method to boost the accuracy of ELCN-YOLOv7 even further. ABSTRACT One of the challenges in the field of photovoltaics (PV) is the automation of defect detection in …
Many current deep learning-based methods for detecting defects in photovoltaic modules focus solely on either detection speed or accuracy, which limits their practical application. To...
We propose a photovoltaic cell defect detection model capable of extracting topological knowledge, aggregating local multi-order dynamic contexts, and effectively...
detection based on computer vision plays a vital role in the manufacturing process of PV cells. This process can advantage safe and high-efficiency operation of the large-scale PV farms. 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 ...
In 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 Partial Network (CSPNet). …
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