Semi-automatic quality inspection of solar cell based on Convolutional Neural Networks

Balzategui, Julen; Eciolaza, Luka; Arana-Arexolaleiba, Nestor; Altube, Jon; Aguerre, J.P.; Legarda-Ereno, Inaki; Apraiz, A.

doi:10.1109/etfa.2019.8869359

Cited by 24 publications

(12 citation statements)

References 21 publications

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“…Then, as defective cells arise, the trained anomaly model will process the samples and output pixel-wise annotations avoiding the time-consuming data annotation task. After some time, when there are enough annotated defective cell samples, a model will be trained in a supervised manner to search for specific features in the images as in our previous works [ 28 , 29 ], obtaining more accurate models.…”

Section: Methodsmentioning

confidence: 99%

“…In addition to classification, in some cases, the location of the defects in the cells is also provided. For example, in our previous works, we used the sliding window approach with a CNN designed for classification to process cell images by patches and accumulate the results in a heatmap-like image, highlighting areas with a high probability of being defective [ 28 ]. Or we explicitly train a Fully Convolutional Network to perform pixel-wise classification [ 29 ].…”

Section: Related Workmentioning

confidence: 99%

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Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network

Balzategui

Eciolaza

Maestro-Watson

2021

Sensors

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Quality inspection applications in industry are required to move towards a zero-defect manufacturing scenario, with non-destructive inspection and traceability of 100% of produced parts. Developing robust fault detection and classification models from the start-up of the lines is challenging due to the difficulty in getting enough representative samples of the faulty patterns and the need to manually label them. This work presents a methodology to develop a robust inspection system, targeting these peculiarities, in the context of solar cell manufacturing. The methodology is divided into two phases: In the first phase, an anomaly detection model based on a Generative Adversarial Network (GAN) is employed. This model enables the detection and localization of anomalous patterns within the solar cells from the beginning, using only non-defective samples for training and without any manual labeling involved. In a second stage, as defective samples arise, the detected anomalies will be used as automatically generated annotations for the supervised training of a Fully Convolutional Network that is capable of detecting multiple types of faults. The experimental results using 1873 Electroluminescence (EL) images of monocrystalline cells show that (a) the anomaly detection scheme can be used to start detecting features with very little available data, (b) the anomaly detection may serve as automatic labeling in order to train a supervised model, and (c) segmentation and classification results of supervised models trained with automatic labels are comparable to the ones obtained from the models trained with manual labels.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network

Balzategui

Eciolaza

Maestro-Watson

2021

Sensors

Self Cite

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“…The methods for defects detection are generally categorized into Photoluminescence (PL) [11], [12] and Electroluminescence (EL). Since some defects in solar cells only display under EL imaging of photovoltaic modules, most current methods [13], [14] use EL for solar cells' defect detection. Table 1 summarizes relevant literature and methods.…”

Section: B Defect Detectionmentioning

confidence: 99%

Automatic Micro-Crack Detection of Polycrystalline Solar Cells in Industrial Scene

et al. 2022

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Solar energy can be a clean and renewable alternative to traditional fuels, which enables its wide application in our life and the industry. However, some defects inevitably occur in the solar cells during production, transportation, and installation, which will reduce the power generation efficiency. In this paper, we propose a ResNet-based micro-crack detection method to detect the micro-cracks on polycrystalline solar cells. Specifically, a novel feature fusion model is introduced to aggregate the low-level features and deep semantically strong features by self-attention mechanism to obtain accurate geometry information. This method boosts the detection accuracy to 99.11%, which significantly surpasses other counterparts, e.g., some state-of-the-art deep neural networks, by a large margin. Since it is difficult for other methods to precisely detect other defect types apart from micro-cracks, we further propose a transfer learning method based on MK-MMD to guide the training process of defect detector with another pre-trained micro-crack detector. With the help of transfer learning, the accuracy of solar cell defect detection increases by 11.6%.

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“…These defect could result by many accidents throughout the OSC fabrication process, for instance, by scratches, uneven morphologies, and so on. There are many possible variations of intelligent systems which are trained to detect patterns, extract image features or identify defects on a surface 4‐11 However, in general, bulk defects, interface defects, and interconnect defects can generate shunt and series resistance within the cell, contrarily it is yet uncertain the peculiar effect of many kind of defects on the OSC functionality 12‐14 . For that reason it is crucial to implement methodologies for detection, localization, and identification of physical defects, not only during the production cycle, but also at design time.…”

Section: Introductionmentioning

confidence: 99%

Organic solar cells defects classification by using a new feature extraction algorithm and an EBNN with an innovative pruning algorithm

et al. 2021

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Physical defects reduce the organic solar cells (OSC) functioning. Throughout the OSC fabrication process, the defects can occur, for instance, by scratches or uneven morphologies. In general, bulk defects, interface defects, and interconnect defects can promote shunt and series resistance of the cell. It is crucial to properly detect and classify such defects and their amount in the structure. Correlating such defects with the performance of the cell is important both during the R&D stages to optimize processes, and for mass production stages where defects detection is an integral part of the production line. For the recognition of texture variations in the scanning electron microscope images caused by these defects is crucial the definition of a set of features for texture representation. Because the low‐order Zernike moments can represent the whole shape of the image and the high‐order Zernike moments can describe the detail. Then, in our case, the feature of the image can be represented by a small number of Zernike moments. In fact, the feature set extracted and described by the Zernike moments are not sensitive to the noises and are hardly redundant. So it possible concentrate the signal energy over a set of few vectors. Finally, for classification, an elliptical basis function neural networks was used. The results show effectiveness of the proposed methodology. In fact, we obtained correct classification of 89.3% over testing data set.

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Semi-automatic quality inspection of solar cell based on Convolutional Neural Networks

Cited by 24 publications

References 21 publications

Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network

Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network

Automatic Micro-Crack Detection of Polycrystalline Solar Cells in Industrial Scene

Organic solar cells defects classification by using a new feature extraction algorithm and an EBNN with an innovative pruning algorithm

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