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oai:ebiltegia.mondragon.edu:20.500.11984/54082024-03-04T10:46:58Zcom_20.500.11984_473col_20.500.11984_478
eBiltegia author Balzategui, Julen author Eciolaza, Luka author Maestro-Watson, Daniel 2021-11-03T11:03:21Z 2021-11-03T11:03:21Z 2021 1424-8220 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=164634 https://hdl.handle.net/20.500.11984/5408 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. eng © 2021 by the authors. Licensee MDPI anomaly detection electroluminescence solar cells neural networks Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network http://purl.org/coar/resource_type/c_6501 
EBILTEGIA-N, MONDRAGON UNIBERTSITATEKO GORDAILU IREKIAN, MATERIALAK UZTEKO ERABILERA LIZENTZIA ETA ESKUBIDEEN LAGAPEN EZ-ESKLUSIBOA

Gordailuan utzitako materialaren egile edo egile eskubideen titular naizen aldetik, Mondragon Unibertsitateari erabilera-baimena eta/edo LAGAPEN EZ-ESKLUSIBOA ematen diot, doan, mundu eremuan eta legezko gehieneko epean, material horren gaineko erreprodukzio, banaketa, komunikazio publiko –elektronikoki eskuragarri jartzeko eskubidea barne– eta eraldatzeko eskubideetarako, hirugarren batzuen esku jartzeko aukera emanez, gordailuan utzitako material horri lotutako dagokion lizentzian ezarritako erabilera baldintzen arabera.

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 URL http://ebiltegia.mondragon.edu/xmlui/bitstream/20.500.11984/5408/1/Anomaly%20Detection%20and%20Automatic%20Labeling%20for%20Solar%20Cell%20Quality%20Inspection%20Based%20on%20Generative%20Adversarial%20Network.pdf File MD5 6c114c925f1bdaadd098041054972c5b 2743459 application/pdf Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network.pdf URL http://ebiltegia.mondragon.edu/xmlui/bitstream/20.500.11984/5408/4/Anomaly%20Detection%20and%20Automatic%20Labeling%20for%20Solar%20Cell%20Quality%20Inspection%20Based%20on%20Generative%20Adversarial%20Network.pdf.txt File MD5 ee4f1124f1c56a6d0e5fa9f7e1bfbfec 70912 text/plain Anomaly Detection and Automatic Labeling for Solar Cell Quality Inspection Based on Generative Adversarial Network.pdf.txt