Open AccessApplication of Convolutional Neural Network in Coffee Capsule Count Aiming Collection System for Recycling
Author(s) -
Henrique Wippel Parucker da Silva,
Gilmário Barbosa dos Santos
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/wvc.2021.18907
Subject(s) - convolutional neural network , garbage collection , garbage , computer science , artificial intelligence , artificial neural network , work (physics) , real time computing , engineering , mechanical engineering , programming language
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%.