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
Solar cells (SCs) are prone to various defects, which affect energy conversion efficiency and even cause fatal damage to photovoltaic modules. In this paper, photoluminescence (PL) imaging is used to visualize SC defects, based on which a detection method based on the YOLOv5 model is explored.
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,
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
In this study, faults in solar panel cells were detected and classified very quickly and accurately using deep learning and electroluminescence images together. A unique and new dataset was created for this study. Monocrystalline and
In this work, we proposed a compact classification framework based on hybrid data augmentation and deep learning models for detection of the defective solar cells. In the proposed method, the limited and imbalanced EL datasets were augmented through various Generative Adversarial Networks (GAN), and defect detection was achieved by
This paper introduces an automatic pipeline for detecting defective cells in EL images of solar modules. The tool performs a perspective transformation of the tilted solar module and either performs direct object detection of defects or crops out single cells for further classification into defect categories. We train different
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 weight assignment, which effectively mitigates the impact of the problem of scant data and data imbalance on model performance; (2) to propose a featur...
Based on its excellent performance, electroluminescence imaging has become the main way of solar cell defect detection. The objective of this study is to enhance solar cell defect detection through a refined YOLOv5 algorithm, incorporating deformable convolution in the CSP module for adaptive learning scales and perceptual field sizes.
Solar cells (SCs) are prone to various defects, which affect energy conversion efficiency and even cause fatal damage to photovoltaic modules. In this paper,
In this work, we proposed a compact classification framework based on hybrid data augmentation and deep learning models for detection of the defective solar cells. In the
This paper introduces an automatic pipeline for detecting defective cells in EL images of solar modules. The tool performs a perspective transformation of the tilted solar
Herein, we propose an adaptive approach for automatic solar cell defect detection and classification based on absolute EL imaging. Specifically, we first develop an unsupervised algorithm to automatically detect defects referring to
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
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
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
An automatic method is proposed for solar cell defect detection and classification. An unsupervised algorithm is designed for adaptive defect detection. A standardized diagnosis scheme is developed for statistical defect classification. Extensive experimental results verify the effectiveness of the proposed method.
An adaptive approach to automatically detect and classify defects in solar cells is proposed based on absolute electroluminescence (EL) imaging. We integrate the convenient automatic detection algorithm with the effective defect diagnosis solution so that in-depth defect detection and classification becomes feasible.
2.3. Proposed solar cell defect detection and classification method Solar cell defect characterization: Generally, the local defects are shown up as dark spots in solar cell EL images, other defect shapes such as micro-crack, large-area failure, break, and finger-interruption are simply regarded as continuous dark spots [20, 21, 51, 53].
The proposed adaptive automatic solar cell defect detection and classification method mainly consists of the following three steps: solar cell EL image preprocessing, adaptive solar cell defect detection, and solar cell defect classification, as shown in Fig. 1.
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.
We published an automatic computer vision pipeline of identifying solar cell defects. Tools can handle field images with a complex background (e.g., vegetation). Tools can be applied to other kinds of defects with transfer learning. We compared the performance of classification and object detection neural networks.
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