The state-of-the-art methods of solar cell surface defects detection based on computer vision, classified into three categories: local scheme, global scheme and local-global scheme based methods, are reviewed. Various types of defects exist in the solar cell surface because of some uncontrollable factors during the process of …
Lichun et al. proposed a solar cell surface quality detection method based on machine vision and artificial neural network in response to the low efficiency …
The automatic detection of surface defects of solar cells can be carried out by using computer vision in a less time-consuming and efficient manner. Many researchers have proposed different methods and algorithms for solar cell surface defect detection. Tsai et al. used Fourier image reconstruction for detection of defects in solar …
The FCM method obtained 94.3% P, 94.98% R, 94.39% DSC, and 94.39% JIR. Also, this method consumed 9.2 ms for detecting and segmenting the cracks in 665 number of solar cell images. Dhimish et al. (2019) analyzed and reviewed various crack detection methods to detect crack regions to improve the energy utilization by the …
6 · Zhang et al. 8 introduced a photovoltaic cell defect detection method leveraging the YOLOV7 model, which is designed for rapid detection. They enhanced the model''s feature extraction ...
A solar cell defect detection method based on Fourier single-pixel imaging (FSI) is proposed to distinguish periodic substrates and defects in reconstruction by projecting illumination patterns with Fourier fringes, in which the removal of substrates reaches 100%. Meanwhile, for the high reflection present on the solar cell surface, the …
A defect detection method based on machine learning for solar cell EL image defect detection that is feasible, effective and high detection rate in the defect detection of solar cells is proposed. It is a very important method in nondestructive testing to detect defects using infrared or electroluminescent (EL) images of solar cell modules.
The defects of solar cell component (SCC) will affect the service life and power generation efficiency. In this paper, the defect images of SCC were taken by the photoluminescence (PL) method and ...
Traditional solar cell defect detection methods usually use Fourier image reconstruction, filtering, clustering, and so on. The defect detection task of solar cells is essentially an image classification method, so with the development of image classification methods based on deep learning, more and more defect detection …
Results indicate that the methods and procedures can accurately detect micro-crack in solar cells with sensitivity, specificity, and accuracy averaging at 97%, 80%, and 88%, respectively. View ...
Defect detection in solar cells plays a significant role in industrial production processes [3]. Conventional methods of solar cell testing require contact with the samples, which can easily cause secondary pollution on the surface of the solar cells during production and processing [4]. In order to avoid this phenomenon, non-destructive ...
A New Approach for Hot Spot Solar Cell Detection based on Multi-level Otsu Algorithm. November 2021; ... This paper presents an effective method for human face detection in thermal imaging. The ...
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 perceptual field size ...
SHA LU et al.: Solar cell surface defect detection based on optimized YOLOv5 FIGURE 1. The number and location distribution of various defects. proposed a photovoltaic panel defect detection method
Many methods have been proposed for detecting defects in PV cells [9], 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 [10]. However, manual visual assessment …
6 · Deep learning methods of PV defect detection. Convolutional neural networks (CNNs) have become a prominent tool in the automatic detection of surface defects in photovoltaic (PV) cells.
Solar cell micro crack detection technique is proposed. • Conventional Electroluminescence (EL) is used to inspect the solar cell cracks. • The techniques is …
This paper reviews all analysis methods of imaging-based and electrical testing techniques for solar cell defect detection in PV systems. This section introduces …
The experimental results show that the improved TCNN can quickly and accurately detect the surface defects of solar cells, and the current overall recognition rate is as high as 96.8%, which is 2.3% higher than the traditional CNN, which verifies the effectiveness of the proposed method.
The use of infrared or electroluminescence(EL) images of solar cell modules for defect detection is a very important method in non-destructive testing. Traditionally, this work is done by skilled technicians, which is time-consuming and susceptible to subjective factors. The surface defect detection method of solar cells based on machine learning has …
This paper proposes an innovative approach that integrates neural networks with photoluminescence detection technology to address defects such as cracks, dirt, …
Defects of solar panels can easily cause electrical accidents. The YOLO v5 algorithm is improved to make up for the low detection efficiency of the traditional defect detection methods. Firstly, it is improved on the basis of coordinate attention to obtain a LCA attention mechanism with a larger target range, which can enhance the sensing …
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 …
Similar and indeterminate defect detection of solar cell surface with heterogeneous texture and complex background is a challenge of solar cell manufacturing. The traditional manufacturing process relies on human eye detection which requires a large number of workers without a stable and good detection effect. In order to solve the …
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 …
Experimental results show that DBN perfectly establishes the mapping relationship, and it can quickly detect defects with a high accuracy. Defects of solar cells are detected mainly by manual operation,and they are difficult to be detected automatically by traditional charge-coupled device( CCD) imaging system. As a training multi-layer neural network,deep …
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 …
It takes a large amount of data to compile the existing CNN-based solar cell detection methods (usually more than 100 images for each cell) as well as highly complex structures for the CNN to work ...
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.
An optimized YOLOv5 model is proposed for more accurate and comprehensive identification of defects in solar cells, which enables the model to perform more accurately while ensuring the real-time requirement of solar cell surface defects detection task. Traditional vision methods for solar cell defect detection have problems such as low …
This study compares two classification methods to assess the ability of the deep learning framework to detect and classify solar defect images automatically. Two-class solar cell dataset after up ...
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 …
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 research focuses on finding a way to distinguish defects in solar cells from the background texture of busbars and fingers. The characteristics of solar cell …
Defect detection of the solar cell surface with texture and complicated background is a challenge for solar cell manufacturing. The classic manufacturing process relies on human eye detection, which requires many workers without a steady and good detection effect. In order to solve the problem, a visual defect detection method based …
6 · Zhang, J. et al. Automatic detection of defective solar cells in electroluminescence images via global similarity and concatenated saliency guided network. IEEE Trans. Ind. Inf. 19, 7335–7345 ...
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 …
Deep learning methods play an important role in surface breakage detection directly due to the self-learning of its own data representation relationship with strong data characterization of the ...
To detect defects on the surface of PV cells, researchers have proposed methods such as electrical characterization [6], electroluminescence imaging [7][8] [9], infrared (IR) imaging [10], etc. EL ...
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 more accurate and comprehensive identification of defects in solar cells. The model firstly integrates five data enhancement methods, namely Mosaic, Mixup, hsv …
The internal defect detection of solar cells indifferent production processes currently adopts manual visual verification on the images captured by electroluminescence or photoluminescence system. ... method. First, the solar cell inspection region is located by shape-based matching. Second, based on the properties of Fourier transform and ...
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 ...
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