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Convolutional neural network models in municipal solid waste classification: towards sustainable management

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Τίτλος: Convolutional neural network models in municipal solid waste classification: towards sustainable management
Συγγραφείς: Castro Bello, Mirna, Roman Padilla, Dominic Brian, Morales Morales, Cornelio, Campos Francisco, Wilfrido, Marmolejo Vega, Carlos Virgilio, Marmolejo Duarte, Carlos Ramiro, Evangelista Alcocer, Yanet, Gutierrez Valencia, Diego Esteban
Συνεισφορές: Universitat Politècnica de Catalunya. Departament de Tecnologia de l'Arquitectura, Universitat Politècnica de Catalunya. QURBIS - Quality of Urban Life: Innovation, Sustainability and Social Engagement
Στοιχεία εκδότη: Multidisciplinary Digital Publishing Institute (MDPI)
Έτος έκδοσης: 2025
Συλλογή: Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge
Θεματικοί όροι: Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic, Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Política i gestió ambiental::Gestió de residus, Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Aplicacions informàtiques a la física i l‘enginyeria, Recycling (Waste, etc.) -- Computer software -- Design and construction, Automatic classification -- Mathematical models -- Evaluation, Deep learning (Machine learning) -- Industrial applications, Robot vision -- Mathematical models -- Software, Object detection, Waste, Convolutional neural networks, Raspberry Pi, Embedded system, Sustainable development, Management, Reciclatge (Residus, etc.) -- Programari -- Disseny i construcció, Classificació automàtica -- Models matemàtics -- Avaluació, Aprenentatge profund -- Aplicacions industrials, Visió artificial (Robòtica) -- Models matemàtics -- Programari
Περιγραφή: Municipal Solid Waste (MSW) management presents a significant challenge for traditional separation practices, due to a considerable increase in quantity, diversity, complexity of types of solid waste, and a high demand for accuracy in classification. Image recognition and classification of waste using computer vision techniques allow for optimizing administration and collection processes with high precision, achieving intelligent management in separation and final disposal, mitigating environmental impact, and contributing to sustainable development objectives. This research consisted of evaluating and comparing the effectiveness of four Convolutional Neural Network models for MSW detection, using a Raspberry Pi 4 Model B. To this end, the models YOLOv4-tiny, YOLOv7-tiny, YOLOv8-nano, and YOLOv9-tiny were trained, and their performance was compared in terms of precision, inference speed, and resource usage in an embedded system with a custom dataset of 1883 organic and inorganic waste images, labeled with Roboflow by de- limiting the area of interest for each object. Image preprocessing was applied, with resizing to 640 × 640 pixels and contrast auto-adjustments. Training considered 85% of images and testing considered 15%. Each training stage was conducted over 100 epochs, adjusting configuration parameters such as learning rate, weight decay, image rotation, and mosaics. The precision results obtained were as follows: YOLOv4-tiny, 91.71%; YOLOv7-tiny, 91.34%; YOLOv8-nano, 93%; and YOLOv9-tiny, 92%. Each model was applied in an embedded system with an HQ camera, achieving an average of 86% CPU usage and an inference time of 1900 ms. This suggests that the models are feasible for application in an intelligent container for classifying organic and inorganic waste, ensuring effective management and promoting a culture of environmental care in society. ; Peer Reviewed ; Postprint (published version)
Τύπος εγγράφου: article in journal/newspaper
Περιγραφή αρχείου: application/pdf
Γλώσσα: English
Relation: https://www.mdpi.com/2071-1050/17/8/3523; Castro, M. [et al.]. Convolutional neural network models in municipal solid waste classification: towards sustainable management. "Sustainability (Basel)", 14 Abril 2025, vol. 17, núm. 8, article 3523.; https://hdl.handle.net/2117/429453
DOI: 10.3390/su17083523
Διαθεσιμότητα: https://hdl.handle.net/2117/429453
https://doi.org/10.3390/su17083523
Rights: http://creativecommons.org/licenses/by/4.0/ ; Open Access ; Attribution 4.0 International
Αριθμός Καταχώρησης: edsbas.2D6CD03F
Βάση Δεδομένων: BASE
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  Data: Convolutional neural network models in municipal solid waste classification: towards sustainable management
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  Data: Universitat Politècnica de Catalunya. Departament de Tecnologia de l'Arquitectura<br />Universitat Politècnica de Catalunya. QURBIS - Quality of Urban Life: Innovation, Sustainability and Social Engagement
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  Data: 2025
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  Data: Municipal Solid Waste (MSW) management presents a significant challenge for traditional separation practices, due to a considerable increase in quantity, diversity, complexity of types of solid waste, and a high demand for accuracy in classification. Image recognition and classification of waste using computer vision techniques allow for optimizing administration and collection processes with high precision, achieving intelligent management in separation and final disposal, mitigating environmental impact, and contributing to sustainable development objectives. This research consisted of evaluating and comparing the effectiveness of four Convolutional Neural Network models for MSW detection, using a Raspberry Pi 4 Model B. To this end, the models YOLOv4-tiny, YOLOv7-tiny, YOLOv8-nano, and YOLOv9-tiny were trained, and their performance was compared in terms of precision, inference speed, and resource usage in an embedded system with a custom dataset of 1883 organic and inorganic waste images, labeled with Roboflow by de- limiting the area of interest for each object. Image preprocessing was applied, with resizing to 640 × 640 pixels and contrast auto-adjustments. Training considered 85% of images and testing considered 15%. Each training stage was conducted over 100 epochs, adjusting configuration parameters such as learning rate, weight decay, image rotation, and mosaics. The precision results obtained were as follows: YOLOv4-tiny, 91.71%; YOLOv7-tiny, 91.34%; YOLOv8-nano, 93%; and YOLOv9-tiny, 92%. Each model was applied in an embedded system with an HQ camera, achieving an average of 86% CPU usage and an inference time of 1900 ms. This suggests that the models are feasible for application in an intelligent container for classifying organic and inorganic waste, ensuring effective management and promoting a culture of environmental care in society. ; Peer Reviewed ; Postprint (published version)
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  Data: https://www.mdpi.com/2071-1050/17/8/3523; Castro, M. [et al.]. Convolutional neural network models in municipal solid waste classification: towards sustainable management. "Sustainability (Basel)", 14 Abril 2025, vol. 17, núm. 8, article 3523.; https://hdl.handle.net/2117/429453
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