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Surface defects in solar cells are various and can be challenging to detect due to the complex background. Before the widespread use of Convolutional Neural Networks (CNNs), manually extracting features for defect detection was a common method in machine vision. The passage discusses the difficulties of this approach.
In this paper, a detection algorithm of surface defects on solar cells is proposed by fusing multi-channel convolution neural networks. The detection results from two different convolution neural networks, i.e., Faster R-CNN and R-FCN, are combined to improve detection precision and position accuracy.
At present, there are three main types of methods for detecting defects in solar cell EL images, i.e., visual inspection, physical methods, and machine vision. Among them, the defect detection method based on machine vision has been greatly developed due to its advantages of good real-time performance, high accuracy, and convenient operation.
These defects will reduce the efficiency of solar cells or even make them completely useless. In this paper, a detection algorithm of surface defects on solar cells is proposed by fusing multi-channel convolution neural networks.
Many existing methods for detecting solar cell defects focus on the analysis of electroluminescence (EL) infrared images, specifically in the 1000–1200 nm wave length range. Chiou et al. (2011) developed a regional growth detection algorithm to extract cracks defects from the captured images.
Conventional solar cell defect detection methods are often interfered with by cell substrates and are difficult to detect under high reflection, especially the overlap of substrates and defects.
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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 …
In this paper, addressing the challenges of low accuracy in detecting small surface defects on solar cells and limited defect categories, a lightweight solar cell detection …
Zhou Q C, Wang B C. Solar cell surface defect detection based on improved YOLOv7[J]. J Comput Appl, 2023, 43(S2): 223−228 doi: 10.11772/j.issn.1001-9081.2023020258 [15] Fu H, …
Manufacturing process and human operational errors may cause small-sized defects, such as cracks, over-welding, and black edges, on solar cell surfaces. These surface …
Similar and indeterminate defect detection of solar cell surface with heterogeneous texture and complex background is a challenge of solar cell manufacturing. The …
M. Zhang, L. Yin: Solar Cell Surface Defect Detection Based on Improved YOLO v5 computer vision based on manual feature extraction and classi˝ers [7] [10]. Tsai et al. …
Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules. In the production process, defect samples occur infrequently and exhibit …
Therefore, surface defect detection of solar cells plays a key role in controlling the quality of solar cell products during manufacturing process (Bulnes et al. 2016). As machine vision develops rapidly, an image-based …
A method for solar cells surface defects detection based on deep learning is proposed. Firstly, deep belief networks(DBN) are established and trained according to the …
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000. Digital Object Identifier 10.1109/ACCESS.2022.0122113 Solar cell surface defect detection based on
In this paper, an SPI-based method for identifying defects on the surface of solar cells is proposed, which solves the problem of high reflection on the surface of solar cells and …
Solar cell surface defects detection based on computer vision. Int. J. Performability Eng., 13 (1048) (2017), 10.23940/ijpe.17.07.p6.10481056. Google Scholar [37] …
In recent years, researchers have conducted extensive studies on defect detection in SC based on deep learning. The focus of these detection networks is on acquiring …
The existing solar cell surface defect detection algorithms based on machine vision are all designed to use various types of mathematical models to carry out the algorithm …
The overall framework of the proposed domain-adaptive solar-cell surface-defect-detection network, shown in Figure 1, mainly consists of an image-level feature alignment (ILFA) module …
Defects in solar cells are generally present on the surface, where the surface is covered with a substrate and transparent tempered glass. Conventional defect detection …
According to the surface quality problem of the solar cells, the machine vision detection system is designed, and the intelligent detection and classification of theSolar cell …
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 …
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 …
For the surface defects of solar cell, which have the characteristics of various shapes, large scale changes, and difficult to detect, a surface defect detection algorithm based …
We propose a method named Convolutional-Vision Transformer Networks (CViT-Net), specifically designed to efficiently integrate local and global features for accurate defect detection in solar …
To increase t he detection precision and location accuracy of so lar cells surface defects, faster-RNN, efficientNet, and autoencoder are three deep learning techn ologies that …
A method for solar cells surface defects detection based on deep learning is proposed rstly,deep belief networks( DBN) are established and trained according to the sample features to obtain …
In this paper, a detection algorithm of surface defects on solar cells is proposed by fusing multi-channel convolution neural networks. The detection results from two different …
To address issues of low detection accuracy and high false-positive and false-negative rates in solar cell defect detection, this paper proposes an optimized solar cell electroluminescent (EL) …
Due to the multiscale characteristics of defects and strong background interference, the automation of solar cell surface defect detection is still a challenge. To …
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 …
Solar cells are playing a significant role in aerospace equipment. In view of the surface defect characteristics in the manufacturing process of solar cells, the common surface …
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...
X. Zhang et al: Surface Defect Detection of Solar Cells Based on Multisc ale Region Proposal Fusion N etwork 2 VOLUME XX, 2017 capabilitie s, Fast R-CNN has a low …
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