Chen et al. (Chen, Pang, Hu & Liu, 2020) designed a visual defect detection method using a multi-spectral deep CNN to address the challenges of detecting similar and indeterminate defects on solar cell surfaces with heterogeneous textures and complex backgrounds.
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, variable defect morphology, and large-scale differences.
This paper presents a benchmark dataset and results for automatic detection and classification using deep learning models trained on 24 defects and features in EL images of crystalline silicon solar cells. The dataset consists of 593 cell images with ground truth masks corresponding to the pixel-level labels for each feature and defect.
Experimental results demonstrate that our approach outperforms traditional methods, providing improved detection accuracy and robustness. The model's ability to generalize well across different defect types and scales makes it a highly effective tool for quality assurance in solar cell manufacturing.
Automatic defect detection and classification in solar cells is the subject of many publications since EL imaging of silicon solar cells was first introduced by Fuyuki et al. for detection of deteriorated areas in solar cells in 2005.
The models tested are effective in detecting, localizing, and quantifying multiple features and defects in EL images of solar cells. These models can thus be used to not only detect the presence of defects, but to track their evolution over time as modules are re-imaged throughout their lifetime.
Solar Cell Surface Defect Detection Based on Optimized Yolov5
A solar cell defect detection method with an improved YOLO v5 algorithm is proposed for the characteristics of the complex solar cell image background, variable defect morphology, and large-scale differences. First, the deformable convolution is incorporated into the CSP module …
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A benchmark dataset for defect detection and classification in ...
Electroluminescence (EL) images enable defect detection in solar photovoltaic (PV) modules that are otherwise invisible to the naked eye, much the same way an x-ray enables a doctor to detect cracks and fractures in bones. This paper presents a benchmark dataset and results for automatic detection and classification using deep ...
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High‐Precision Defect Detection in Solar Cells Using YOLOv10 …
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 images, annotated with 12 ...
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(PDF) Deep learning-based method for defect detection in ...
To achieve defect detection in bare polycrystalline silicon solar cells under electroluminescence (EL) conditions, we have proposed ASDD-Net, a deep learning algorithm evaluated offline on EL images.
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Deep Learning-Based Algorithm for Multi-Type Defects Detection in Solar ...
In [13], a public dataset of solar cells is provided that contains 2,624 solar cell images and two approaches are proposed to classify the EL images. In [14], a fusion model of Faster R-CNN and R-FCN is proposed to detect solar cell surface defects.
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Research on Online Defect Detection Method of Solar Cell …
For solar cell defect detection, Chen et al. [] proposed a cell crack defect detection scheme based on structure perception designing the structure similarity measure (SSM) function, using the nonmaximum value suppression method to extract candidate crack defects, the proposed SSM function has stronger crack defect protrusion and suppression of …
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Multi-scale YOLOv5 for solar cell defect detection
CHEN Yafang,LIAO Fei,HUANY Xinyu,et al.Multi-scale YOLOv5 for solar cell defect detection[J].Optics and Precision Engineering,2023,31(12):1804-1815.
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A benchmark dataset for defect detection and classification in ...
Electroluminescence (EL) images enable defect detection in solar photovoltaic (PV) modules that are otherwise invisible to the naked eye, much the same way an x-ray …
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Deep Learning-Based Algorithm for Multi-Type Defects Detection in Solar ...
PDF | On Jan 1, 2022, Wuqin Tang and others published Deep Learning-Based Algorithm for Multi-Type Defects Detection in Solar Cells with Aerial EL Images for Photovoltaic Plants | Find, read and ...
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Solar Cell Surface Defect Detection Based on Optimized YOLOv5
The results show that the optimized model achieves an mAP of 96.1% on the publicly available dichotomous ELPV dataset, and can identify and locate a variety of common defects in the …
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Solar Cell Surface Defect Detection Based on Optimized Yolov5
A solar cell defect detection method with an improved YOLO v5 algorithm is proposed for the characteristics of the complex solar cell image background, variable defect morphology, and large-scale differences. First, the deformable convolution is …
Learn More
Multi-scale YOLOv5 for solar cell defect detection
Compared with other algorithms, the improved YOLOv5 model can accurately detect cracks and break defects in EL solar cells, satisfying the demand for real-time, high-precision defect detection under industrial conditions in photovoltaic power plants.
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Accurate detection and intelligent classification of solar cells ...
Addressing this issue, this paper combines neural networks with photoluminescence detection technology and proposes a novel neural network model for the …
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Accurate detection and intelligent classification of solar cells ...
Addressing this issue, this paper combines neural networks with photoluminescence detection technology and proposes a novel neural network model for the classification and grading of defects in solar cells. Firstly, the YOLOv5 model is optimized and adjusted for algorithm and network structure.
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Solar Cell Surface Defect Detection Based on Optimized YOLOv5
The results show that the optimized model achieves an mAP of 96.1% on the publicly available dichotomous ELPV dataset, and can identify and locate a variety of common defects in the PVEL-AD dataset, while the mAP can reach 87.4%, an improvement of 10.38% compared with the original YOLOv5 model, which enables the model to perform more accurately ...
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High-Precision Defect Detection in Solar Cells Using YOLOv10 …
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 images annotated with 12 distinct defect types, our model integrates Compact Inverted Blocks (CIBs) and Partial Self-Attention (PSA) modules to enhance feature ...
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Optimizing feature extraction and fusion for high-resolution defect ...
In this paper, we propose a novel architecture for defect detection in electroluminescent images of polycrystalline silicon solar cells, addressing the challenges posed by subtle and dispersed defects. Our model, based on a modified Swin Transformer, incorporates key innovations that enhance feature extraction and fusion. We replace ...
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A PV cell defect detector combined with transformer and …
Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor-intensive and...
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High-Precision Defect Detection in Solar Cells Using …
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 images annotated with 12 distinct defect types, our …
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Solar Cell Surface Defect Detection Based on Improved YOLO v5
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, variable defect morphology, and large-scale differences. First, the deformable convolution is incorporated into the CSP module to achieve an adaptive learning scale and ...
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Enhanced YOLOv5 Algorithm for Defect Detection in Solar Cells
To address challenges in detecting defects of varying scales in solar cells, an enhanced YOLOv5 algorithm is proposed. This algorithm integrates the Convolutional Block Attention Module (CBAM) to improve feature extraction, incorporates the Bi-directional Feature Pyramid Network (BiFPN) for refined feature fusion, and introduces the FasterNet ...
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Multi-scale YOLOv5 for solar cell defect detection
Compared with other algorithms, the improved YOLOv5 model can accurately detect cracks and break defects in EL solar cells, satisfying the demand for real-time, high …
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A Large-scale Evaluation of Pretraining Paradigms for the Detection …
Pretraining has been shown to improve performance in many domains, including semantic segmentation, especially in domains with limited labelled data. In this work, we perform a large-scale evaluation and benchmarking of various pretraining methods for Solar Cell Defect Detection (SCDD) in electroluminescence images, a field with limited labelled datasets. We …
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Optimizing feature extraction and fusion for high-resolution defect ...
In this paper, we propose a novel architecture for defect detection in electroluminescent images of polycrystalline silicon solar cells, addressing the challenges …
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SOLAR CELL DEFECT DETECTION AND ANALYSIS SYSTEM USING …
The author in [4] presents an innovative solar cell defect detection system emphasizing portability and low computational power. The research utilizes K-means, MobileNetV2, and linear discriminant algorithms to cluster solar cell images and create customized detection models for each cluster. This method effectively differentiates between
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An efficient CNN-based detector for photovoltaic module cells defect ...
Tsai et al. [13] utilized fourier image reconstruction for defect detection in solar cells. However, these traditional methods based on machine learning rely on feature engineering and often struggle to achieve satisfactory results. Recently, image processing methods based on convolutional neural network (CNN) have achieved significant breakthroughs due to their …
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Solar Cell Surface Defect Detection Based on Improved YOLO v5
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, …
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A PV cell defect detector combined with transformer and attention ...
Automated 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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Enhanced YOLOv5 Algorithm for Defect Detection in Solar Cells
To address challenges in detecting defects of varying scales in solar cells, an enhanced YOLOv5 algorithm is proposed. This algorithm integrates the Convolutional Block Attention Module …
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A PV cell defect detector combined with transformer and …
El Yanboiy et al. 7 implemented real-time solar cell defect detection using the YOLOv5 algorithm, improving the stability and efficiency of solar systems. Jha et al. 24 conducted a comprehensive ...
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