Algorithm Based on Deep Learning Techniques for Classification of Solid Waste in Recycling Plants

Cesar Pena Carrillo; Ciro Rodriguez; Martha Gonzales Loli; Amador Vivar Recarte; Diego Ampuero Aldorarin; Diego Rodriguez

doi:10.1109/CICN59264.2023.10402176

Algorithm Based on Deep Learning Techniques for Classification of Solid Waste in Recycling Plants

Cesar Pena Carrillo, Ciro Rodriguez, Martha Gonzales Loli, Amador Vivar Recarte, Diego Ampuero Aldorarin, Diego Rodriguez

Facultad de Derecho y Ciencia Política

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

The imperatives of environmental sustainability and resource efficiency necessitate advancing recycling technologies, among which solid waste classification is a pivotal process. Traditional manual sorting methods, hindered by inefficiency, cost, and scalability issues, have given way to innovative solutions employing deep learning algorithms and specialized software, promising to revolutionize the solid waste management sector. This manuscript explores the burgeoning domain of algorithm-based classification systems for solid waste, specifically focusing on their application within recycling plants. It comprehensively studies these systems' effectiveness, precision, and environmental impact while examining their implementation's particularities in various contexts, including the Peruvian districts. Comparative analysis of CNN architectures suggests that while ResNet18 was sufficient, Inception-ResNet could yield higher accuracy due to its complexity and depth if computational resources were not a limiting factor.

Idioma original	Inglés
Título de la publicación alojada	Proceedings - 2023 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	211-217
Número de páginas	7
ISBN (versión digital)	9798350324433
DOI	https://doi.org/10.1109/CICN59264.2023.10402176
Estado	Publicada - 2023
Evento	15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023 - Bangkok, Tailandia Duración: 22 dic. 2023 → 23 dic. 2023

Serie de la publicación

Nombre	Proceedings - 2023 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023

Conferencia

Conferencia	15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023
País/Territorio	Tailandia
Ciudad	Bangkok
Período	22/12/23 → 23/12/23

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10.1109/CICN59264.2023.10402176

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Carrillo, C. P., Rodriguez, C., Loli, M. G., Recarte, A. V., Aldorarin, D. A., & Rodriguez, D. (2023). Algorithm Based on Deep Learning Techniques for Classification of Solid Waste in Recycling Plants. En Proceedings - 2023 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023 (pp. 211-217). (Proceedings - 2023 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CICN59264.2023.10402176

Carrillo, Cesar Pena ; Rodriguez, Ciro ; Loli, Martha Gonzales et al. / Algorithm Based on Deep Learning Techniques for Classification of Solid Waste in Recycling Plants. Proceedings - 2023 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 211-217 (Proceedings - 2023 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023).

@inproceedings{fbc3aa480a16472c900f52ff8277708c,

title = "Algorithm Based on Deep Learning Techniques for Classification of Solid Waste in Recycling Plants",

abstract = "The imperatives of environmental sustainability and resource efficiency necessitate advancing recycling technologies, among which solid waste classification is a pivotal process. Traditional manual sorting methods, hindered by inefficiency, cost, and scalability issues, have given way to innovative solutions employing deep learning algorithms and specialized software, promising to revolutionize the solid waste management sector. This manuscript explores the burgeoning domain of algorithm-based classification systems for solid waste, specifically focusing on their application within recycling plants. It comprehensively studies these systems' effectiveness, precision, and environmental impact while examining their implementation's particularities in various contexts, including the Peruvian districts. Comparative analysis of CNN architectures suggests that while ResNet18 was sufficient, Inception-ResNet could yield higher accuracy due to its complexity and depth if computational resources were not a limiting factor.",

keywords = "CNN, ResNet18, deep learning, recycling, solid waste",

author = "Carrillo, {Cesar Pena} and Ciro Rodriguez and Loli, {Martha Gonzales} and Recarte, {Amador Vivar} and Aldorarin, {Diego Ampuero} and Diego Rodriguez",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023 ; Conference date: 22-12-2023 Through 23-12-2023",

year = "2023",

doi = "10.1109/CICN59264.2023.10402176",

language = "English",

series = "Proceedings - 2023 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "211--217",

booktitle = "Proceedings - 2023 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023",

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Carrillo, CP, Rodriguez, C, Loli, MG, Recarte, AV, Aldorarin, DA & Rodriguez, D 2023, Algorithm Based on Deep Learning Techniques for Classification of Solid Waste in Recycling Plants. En Proceedings - 2023 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023. Proceedings - 2023 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023, Institute of Electrical and Electronics Engineers Inc., pp. 211-217, 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023, Bangkok, Tailandia, 22/12/23. https://doi.org/10.1109/CICN59264.2023.10402176

Algorithm Based on Deep Learning Techniques for Classification of Solid Waste in Recycling Plants. / Carrillo, Cesar Pena; Rodriguez, Ciro; Loli, Martha Gonzales et al.
Proceedings - 2023 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 211-217 (Proceedings - 2023 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Algorithm Based on Deep Learning Techniques for Classification of Solid Waste in Recycling Plants

AU - Carrillo, Cesar Pena

AU - Rodriguez, Ciro

AU - Loli, Martha Gonzales

AU - Recarte, Amador Vivar

AU - Aldorarin, Diego Ampuero

AU - Rodriguez, Diego

PY - 2023

Y1 - 2023

N2 - The imperatives of environmental sustainability and resource efficiency necessitate advancing recycling technologies, among which solid waste classification is a pivotal process. Traditional manual sorting methods, hindered by inefficiency, cost, and scalability issues, have given way to innovative solutions employing deep learning algorithms and specialized software, promising to revolutionize the solid waste management sector. This manuscript explores the burgeoning domain of algorithm-based classification systems for solid waste, specifically focusing on their application within recycling plants. It comprehensively studies these systems' effectiveness, precision, and environmental impact while examining their implementation's particularities in various contexts, including the Peruvian districts. Comparative analysis of CNN architectures suggests that while ResNet18 was sufficient, Inception-ResNet could yield higher accuracy due to its complexity and depth if computational resources were not a limiting factor.

AB - The imperatives of environmental sustainability and resource efficiency necessitate advancing recycling technologies, among which solid waste classification is a pivotal process. Traditional manual sorting methods, hindered by inefficiency, cost, and scalability issues, have given way to innovative solutions employing deep learning algorithms and specialized software, promising to revolutionize the solid waste management sector. This manuscript explores the burgeoning domain of algorithm-based classification systems for solid waste, specifically focusing on their application within recycling plants. It comprehensively studies these systems' effectiveness, precision, and environmental impact while examining their implementation's particularities in various contexts, including the Peruvian districts. Comparative analysis of CNN architectures suggests that while ResNet18 was sufficient, Inception-ResNet could yield higher accuracy due to its complexity and depth if computational resources were not a limiting factor.

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KW - deep learning

KW - recycling

KW - solid waste

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U2 - 10.1109/CICN59264.2023.10402176

DO - 10.1109/CICN59264.2023.10402176

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BT - Proceedings - 2023 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Carrillo CP, Rodriguez C, Loli MG, Recarte AV, Aldorarin DA, Rodriguez D. Algorithm Based on Deep Learning Techniques for Classification of Solid Waste in Recycling Plants. En Proceedings - 2023 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 211-217. (Proceedings - 2023 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023). doi: 10.1109/CICN59264.2023.10402176

Algorithm Based on Deep Learning Techniques for Classification of Solid Waste in Recycling Plants

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