Abstract
As a clean and renewable energy, solar has great development and utilization value. The production instability will affect the solar cells’ photoelectric conversion efficiency, so it is necessary to classify color difference cells. Currently, color difference classification is still detected by manual method, which is low efficient and depends on subjectivity and experience and hard to meet the production requirements, so it’s urgent to find a new method to detect and classify color difference automatically. This paper introduces an effective way to achieve that by us-ing SVM. Using HSI model to calculate hue, saturation and intensity color histograms, 12 color feature vectors are extracted from the histograms. After experiments and simulation analyses, some feature vectors in these 12 features are as input vectors to SVM. After training the train set, the result of prediction set classification can be predicted and the accuracy rate can reach 94.79%, which shows that this method is effective.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Duenas, S., Perez, E., Castan, H., et al.: The role of defects in solar cells: control and detection defects in solar cells, 301–304 (2013)
Du, J., Zhang, X., Hu, Q.: An automatic condition detection approach for quality assurance in solar cell manufacturing processes. IEEE Robot. Autom. Lett. 2(3), 1825–1831 (2017)
Zheng, L., Li, X., Yan, X., Li, F., Zheng, X., Li, W.: Lip color classification based on support vector machine and histogram. In: 3rd International Congress on Image and Signal Processing, Yantai, pp. 1883–1886 (2010)
Morillas, J.R.A., Garcia, I.C., Zolzer, U.: Ship detection based on SVM using color and texture features. In: 2015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, pp. 343–350 (2015)
Kavitha, J.C., Suruliandi, A.: Texture and color feature extraction for classification of melanoma using SVM. In: 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE 2016), Kovilpatti, pp. 1–6 (2016)
Xu, H., Ying, Y.: Spectra coupled with color features to determine sugar content of fragrant pears using LS-SVM. In: IEEE/SICE International Symposium on System Integration (SII), Kyoto, pp. 197–201 (2011)
Zhang, C., Wang, P.: A new method of color image segmentation based on intensity and hue clustering. In: Proceedings 15th International Conference on Pattern Recognition, ICPR-2000, Barcelona, vol. 3, pp. 613–616 (2000)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Publishing House of Electronics Industry, Beijing (2010)
Yu, Z., Chen, W., Guo, X., Chen, X., Sun, C.: Analog network-coded modulation with maximum Euclidean distance: mapping criterion and constellation design. IEEE Access 5, 18271–18286 (2017)
Yin, Z., Liu, J., Krueger, M., Gao, H.: Introduction of SVM algorithms and recent applications about fault diagnosis and other aspects. In: 2015 IEEE 13th International Conference on Industrial Informatics (INDIN), Cambridge, pp. 550–555 (2015)
Osuna, E., Freund, R., Girosit, F.: Training support vector machines: an application to face detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, pp. 130–136 (1997)
Demir, B., Bruzzone, L.: Fast and accurate image classification with histogram based features and additive kernel SVM. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, pp. 2350–2353 (2015)
Ajay, A., Dixon, K.D.M., Sowmya, V., Soman, K.P.: Aerial image classification using GURLS and LIBSVM. In: 2016 International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, pp. 0396–0401 (2016)
Pooja, A., Mamtha, R., Sowmya, V., Soman, K.P.: X-ray image classification based on tumor using GURLS and LIBSVM 2016 International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, pp. 0521–0524 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, J., Liu, T. (2018). Research on Surface Color Difference of Solar Cells Based on Support Vector Machine. In: Zhai, G., Zhou, J., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2017. Communications in Computer and Information Science, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-10-8108-8_4
Download citation
DOI: https://doi.org/10.1007/978-981-10-8108-8_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8107-1
Online ISBN: 978-981-10-8108-8
eBook Packages: Computer ScienceComputer Science (R0)