The Use of Deep Learning in Verifying the Functioning of LEDs
DOI:
https://doi.org/10.31686/ijier.vol8.iss7.2458Keywords:
Computer Vision, Deep Learning, Industry 4.0, LED RecognitionAbstract
This article aims to bring an alternative to carrying out manual tests of devices mounted on a production line. One of the tests done by the operator is to find out if the LEDs are present on the device being turned on and working correctly. Image processing techniques were applied in the elaboration of the dataset and the use of Convolutional Neural Networks for the classification of the colors presented by the LEDs and the recognition of their operation. An accuracy of 99.25% was obtained with a low percentage of false positives and true negatives. There were no difficulties in porting the model built to a small computer.
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References
(1) ABDI (2020). Agenda brasileira para a indústria 4.0. http://www.industria40. gov.br/.
(2) Banik, P. P., Saha, R., and Kim, K. (2017). Improvement of color detection by regression analysis of visual-mimo system. In 2017 IEEE Globecom Workshops (GC Wkshps), pages 1–4. DOI: https://doi.org/10.1109/GLOCOMW.2017.8269141
(3) Brighenti, J. R. N. (2006). Simulação e otimização de uma linha de manufatura em fase de projeto. In f. Dissertação (Mestrado em Engenharia de Produção), page 115. Universidade Federal de Itajubá.
(4) Cardoso and Lopes, R. (2018). Aplicação do indicador de eficácia global (oee) de equipamentos em uma linha de produção automatizada para análises clínicas. PPGCF Mestrado em Ciências Farmacêuticas (Dissertações).
(5) Chollet, F. (2018). Deep Learming with Python. Manning Publications, Nova Iorque, Estados Unidos, 1 edition.
(6) Chollet, F. et al. (2015). Keras. https://keras.io.
(7) de Pádua Braga, A., Ludermir, T. B., and de Leon Ferreira Carvalho, A. C. P. (2007). Redes Neurais Artificiais. LTC, Rio de Janeiro, Brasil, 2 edition.
(8) Dias, F. W. L., da Silva Souza, J., Barbacena, I. L., and da Silva Moreira, C. (2016). Sistema de detecção de cores usando arduino e app inventor destinado a pessoas com baixa acuidade visual.
(9) Faceli, K. et al. (2011). Inteligência Artificial. LTC, Rio de Janeiro, Brasil, 1 edition.
(10) Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learming. The MIT Press, Massachusetts, Estados Unidos, 1 edition.
(11) Gulli, A. and Pal, S. (2017). Deep Learning with Keras. Packt Publishing, Birmingham, Reino Unido, 1 edition.
(12) Howard, A., Sandler, M., Chu, G., Chen, L., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., Le, Q. V., and Adam, H. (2019). Searching for mobilenetv3. CoRR, abs/1905.02244. DOI: https://doi.org/10.1109/ICCV.2019.00140
(13) Khan, S. et al. (2018). A Guide to Convolutional Neural Networks for Computer Vision. Morgan & Claypool, Austrália do Oeste, Austrália, 1 edition. DOI: https://doi.org/10.2200/S00822ED1V01Y201712COV015
(14) Lima, A. G. and Pinto, G. S. (2019). IndÚstria 4.0. Revista Interface Tecnológica, 16(2):299–311. DOI: https://doi.org/10.31510/infa.v16i2.642
(15) Marques, V. G. O. (2017). Avaliação do desempenho das redes neurais convolucionais na detecção de ovos de esquistossomose. Monografia.
(16) Mergulhão, E. W. T., Andrade, S. H. M. S., and do Nascimento, J. O. (2019). Um modelo computacional baseado em redes neurais artificiais para auxiliar o reconhecimento de cores por portadores de daltonismo. Blucher Physics Proceedings, 6(1):61 – 66. DOI: https://doi.org/10.5151/ecfa2019-14
(17) Monard, M. C. and Baranauskas, J. A. (2003). Conceitos sobre aprendizado de máquina. In Sistemas Inteligentes Fundamentos e Aplicações, pages 89–114. Manole Ltda, Barueri-SP, 1 edition.
(18) Polzer, A., Gaberl, W., Davidovic, M., and Zimmermann, H. (2011). Integrated filterless bicmos sensor for rgb-led color determination. In SENSORS, 2011 IEEE, pages 1937–1940. DOI: https://doi.org/10.1109/ICSENS.2011.6126957
(19) Russell, S. and Norvig, P. (2013). Inteligência Artificial. Elsevier, Rio de Janeiro, Brasil, 3 edition.
(20) Simões, A. (2000). Segmentação de imagens por classificação de cores: uma abordagem neural. Master’s thesis, Escola Politécnica da Universidade de São Paulo (Poli/USP).
(21) Solem, J. E. (2012). Programming Computer Vision with Python. O’Reilly Media, Califónia, Estados Unidos, 1 edition.
(22) Tsai, M., Chang, S., Lee, C., and Chou, C. (2011). Color quality inspection and compensation for color led display modules. In 2011 International Conference on Machine Learning and Cybernetics, volume 4, pages 1720–1725. DOI: https://doi.org/10.1109/ICMLC.2011.6016985
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Accepted 2020-06-22
Published 2020-07-01