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Artificial Intelligence in Automated Sorting in Trash Recycling. / Costa, Bernardo S.; Bernardes, Aiko C. S.; Pereira, Julia V. A. et al.
Anais do XV Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2018). 2018. p. 198-205.
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Costa, BS, Bernardes, ACS, Pereira, JVA, Zampa, VH, Pereira, VA, Matos, GF
, Almeida Soares, E, Soares, CL & Silva, AF 2018,
Artificial Intelligence in Automated Sorting in Trash Recycling. in
Anais do XV Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2018). pp. 198-205.
https://doi.org/10.5753/eniac.2018.4416APA
Costa, B. S., Bernardes, A. C. S., Pereira, J. V. A., Zampa, V. H., Pereira, V. A., Matos, G. F.
, Almeida Soares, E., Soares, C. L., & Silva, A. F. (2018).
Artificial Intelligence in Automated Sorting in Trash Recycling. In
Anais do XV Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2018) (pp. 198-205)
https://doi.org/10.5753/eniac.2018.4416Vancouver
Author
Bibtex
@inproceedings{752823162bda43a3b1ae9bccda8701a1,
title = "Artificial Intelligence in Automated Sorting in Trash Recycling",
abstract = "A computer vision approach to classify garbage into recycling categories could be an efficient way to process waste. This project aims to take garbage waste images and classify them into four classes: glass, paper, metal and, plastic. We use a garbage image database that contains around 400 images for each class. The models used in the experiments are Pre-trained VGG-16 (VGG16), AlexNet, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and, Random Forest (RF). Experiments showed that our models reached accuracy of around 93%.",
author = "Costa, {Bernardo S.} and Bernardes, {Aiko C. S.} and Pereira, {Julia V. A.} and Zampa, {Vitoria H.} and Pereira, {Vitoria A.} and Matos, {Guilherme F.} and {Almeida Soares}, Eduardo and Soares, {Claiton L.} and Silva, {Alexandre F.}",
year = "2018",
month = oct,
day = "22",
doi = "10.5753/eniac.2018.4416",
language = "English",
pages = "198--205",
booktitle = "Anais do XV Encontro Nacional de Intelig{\^e}ncia Artificial e Computacional (ENIAC 2018)",
}
RIS
TY - GEN
T1 - Artificial Intelligence in Automated Sorting in Trash Recycling
AU - Costa, Bernardo S.
AU - Bernardes, Aiko C. S.
AU - Pereira, Julia V. A.
AU - Zampa, Vitoria H.
AU - Pereira, Vitoria A.
AU - Matos, Guilherme F.
AU - Almeida Soares, Eduardo
AU - Soares, Claiton L.
AU - Silva, Alexandre F.
PY - 2018/10/22
Y1 - 2018/10/22
N2 - A computer vision approach to classify garbage into recycling categories could be an efficient way to process waste. This project aims to take garbage waste images and classify them into four classes: glass, paper, metal and, plastic. We use a garbage image database that contains around 400 images for each class. The models used in the experiments are Pre-trained VGG-16 (VGG16), AlexNet, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and, Random Forest (RF). Experiments showed that our models reached accuracy of around 93%.
AB - A computer vision approach to classify garbage into recycling categories could be an efficient way to process waste. This project aims to take garbage waste images and classify them into four classes: glass, paper, metal and, plastic. We use a garbage image database that contains around 400 images for each class. The models used in the experiments are Pre-trained VGG-16 (VGG16), AlexNet, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and, Random Forest (RF). Experiments showed that our models reached accuracy of around 93%.
U2 - 10.5753/eniac.2018.4416
DO - 10.5753/eniac.2018.4416
M3 - Conference contribution/Paper
SP - 198
EP - 205
BT - Anais do XV Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2018)
ER -
