Texture defect inspection for silicon solar cell

Zhang, Wu-jie; Li, Di; Feng, Yaokai

doi:10.3724/sp.j.1087.2010.02702

Cited by 5 publications

(7 citation statements)

References 1 publication

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“…The article mainly focuses on the surface defect detection in silicon panels [1]. At present, the common methods in the field of defect detection are manual visual inspection, infrared inspection, machine vision inspection, etc.…”

Section: Study On Surface Defect Detection In Silicon Panelsmentioning

confidence: 99%

“…There are many methods for detecting surface defects in silicon panels, such as the ray method [1], acoustic method [2], mechanical method, artificial vision method, and machine vision method [3]. Manual detection is greatly affected by experience and subjective factors.…”

Section: Study On Surface Defect Detection In Silicon Panelsmentioning

confidence: 99%

“…Different from the methods above, Rosch proposed the problem of defect detection on the surface of polymer silicon panels and proposed dark lock-in thermography (DLIT), electroluminescence (EL) imaging, and photoluminescence/reflection imaging (PLI/RI) complemented by optical imaging (OI) [15]. And in order to detect texture defect in silicon solar cells, Wujie Zhang proposed a texture defect detection method combining the steerable filters, which is more effective for detecting texture defects than wavelet filter methods [1].…”

Section: Detection Of Solar Panels Surface Defects By El or Plmentioning

confidence: 99%

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Research on Image Defect Detection of Silicon Panel Based on Prewitt and Canny Operator

Zhang

Wang

et al. 2021

Front. Phys.

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The silicon panel is the core component of photovoltaic power generation, whose surface quality is related to its service life and power generation efficiency. However, microcracks, fragments, incomplete welding, broken grids, and other defects often occur in industrial production. The edge detection algorithm is usually used to detect defects in silicon panels, but the common edge detection algorithm has an impact on defect detection because of the grid shadow of the panel. The current mainstream defect detection algorithm based on convolutional neural network requires a large number of positive and negative samples of image data sets for pretraining the model, which consumes a lot of time and GPU computing power, and the steps are cumbersome. To solve the problem, a defect detection method based on Prewitt and Canny operators is proposed in this article. In this method, Prewitt and Canny operators are combined to eliminate the effect of grids on the detection. The microcrack defects and their specific positions can be detected efficiently and intuitively, therefore improving the detection accuracy. The experimental results indicate that the purity and integrity of the defect profile of the image processed by the algorithm are greatly improved. The foreground edge is clear, and the defect recognition accuracy is higher, which effectively prevent the impact of grid shadow on weld testing.

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Section: Study On Surface Defect Detection In Silicon Panelsmentioning

confidence: 99%

Section: Study On Surface Defect Detection In Silicon Panelsmentioning

confidence: 99%

Section: Detection Of Solar Panels Surface Defects By El or Plmentioning

confidence: 99%

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Research on Image Defect Detection of Silicon Panel Based on Prewitt and Canny Operator

Zhang

Wang

et al. 2021

Front. Phys.

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“…( 2)Obtained the two variables of the gray value on plane-coordinate system in S-passage model; calculated the sum of the two variables and got the gradient image. (3)Did threshold segmentation operation (the threshold value was 35 to the gradient image, picked out the areas with obvious gradient value variation [6]; these areas were regarded as suspected spots. We decomposed these areas into pieces of regions.…”

Section: Spot Defects Detectionmentioning

confidence: 99%

Surface Spot Defects Inspection of Multi-Crystalline Silicon Wafers Based on HALCON

Miao

et al. 2014

AMR

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As a common kind of surface defects on multi-crystalline silicon solar cells (about 65% of total defects). Based on HALCON image processing library, an automatic silicon wafer surface spot defects detection and classification system has been developed: captured color images via CCD image sensor, located wafers and spots, extracted the spot features, computed and separated the qualified regions from the unqualified ones by SVM classifier. The experimental result showed a high accuracy of 95.7% and fast image analyzing and classifying process (less than 600ms).

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“…At present, using artificial way is still a method of surface quality detection for many solar cell manufacturers [1] ,which influences accuracy of result because of depending mainly on some unreliable factors of the working staff, such as visual judgment, subjective consciousness, physical condition and fatigue etc. Moreover, this kind of the method can't meet real-time monitoring requirements of the production quality process, whereas usually it is not only time-consuming but also low efficiency without realizing online defect detection during high speed production [2] .…”

Section: Introductionmentioning

confidence: 99%

Research of Solar Cell Surface Defect Detection System Based on Machine Vision

Feng

Zhou

2013

AMR

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According to the surface quality problem of the solar cells, the machine vision detection system is designed. Concept design of the visual inspection system, hardware configuration and software work process are described in detail. In the experimental process, solar cell images are collected in the motion state, the image characteristics of all kinds of damage are extracted, and the least squares support vector machine algorithm is used to construct the solar cell defect recognition model, the intelligent detection and classification of the solar cells can be achieved. Practice dictates that the system is effective to detect the surface defect of solar cells, guide the production process and improve the quality of products.

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Texture defect inspection for silicon solar cell

Cited by 5 publications

References 1 publication

Research on Image Defect Detection of Silicon Panel Based on Prewitt and Canny Operator

Research on Image Defect Detection of Silicon Panel Based on Prewitt and Canny Operator

Surface Spot Defects Inspection of Multi-Crystalline Silicon Wafers Based on HALCON

Research of Solar Cell Surface Defect Detection System Based on Machine Vision

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