Application of Convolutional Neural Network in Coffee Capsule Count Aiming Collection System for Recycling
Resumo
The coffee capsules brought practicality and speed in the preparation of the drink. However, with its popularization came a major environmental problem, the generation of a large amount of garbage, which for 2021 has an estimated 14 thousand tons of garbage, only coming from the capsules. To avoid this disposal it is necessary to recycle them, however it is not a trivial job, since they are composed of various materials, as well as the collection of these capsules presents challenges. Therefore, a collection system is of great value, which, in addition to being automated, generates bonuses proportional to the quantity of discarded capsules. This work is dedicated preliminary tests on the development of such a system using a convolutional neural network for the detection of coffee capsules. This algorithm was trained with two image sets, one containing images with reflection and the other without, which presented an accuracy of approximately 97%.
Referências
S. Talita, “História do café - a origem e a trajetória da bebida no mundo.” 2016. [Online]. Available: https://www.graogourmet.com/blog/historia-do-cafe/
E. C. e Silva et al., “Governança privada e sustentabilidade na indústria do café,” ”Revista agroalimentaria”, vol. 25, no. 48, pp. 35–51, 2015.
D. Bolton, “Precision manufacturing is essential to capsule success,” 2015.
K. P. Silveira, “Iniciativas sustentáveis: Nespresso - transparência pela sustentabilidade.” 2018. [Online]. Available: https://www.fiesp.com.br/indices-pesquisas-epublicacoes/case-nespresso/
A. Geron, Hands-On Machine Learning with Scikit- Learn and TensorFlow, Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly, 2017.
N. Buduma and N. Locascio, Fundamentals of deep learning: designing next-generation machine intelligence algorithms. O’Reilly, 2017.
S. Haykin, Neural networks and learning machines. McMaster University, Canada: Pearson, 2009.
V. Phung and E. Rhee, “A deep learning approach for classification of cloud image patches on small datasets,” Journal of Information and Communication Convergence Engineering, vol. 16, pp. 173–178, 01 2018.
COGNEX, “Iluminação de visão industrial,” 2016. [Online]. Available: https://www.cognex.com/pt-br/whatis/machine-vision/components/lighting
N.-F. Huang, D.-L. Chou, and C.-A. Lee, “Real-time classification of green coffee beans by using a convolutional neural network,” in 2019 3rd International Conference on Imaging, Signal Processing and Communication (ICISPC), 2019, pp. 107–111.
