Convolutional Neural Network Models in Municipal Solid Waste Classification: Towards Sustainable Management

Mirna Castro-Bello, Dominic Brian Roman-Padilla, Cornelio Morales-Morales (), Wilfrido Campos-Francisco, Carlos Virgilio Marmolejo-Vega, Carlos Marmolejo-Duarte, Yanet Evangelista-Alcocer and Diego Esteban Gutiérrez-Valencia
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Mirna Castro-Bello: Technological Institute of Chilpancingo, National Institute of Technology of Mexico, Chilpancingo de los Bravo 39090, Mexico
Dominic Brian Roman-Padilla: Technological Institute of Chilpancingo, National Institute of Technology of Mexico, Chilpancingo de los Bravo 39090, Mexico
Cornelio Morales-Morales: Technological Institute of San Juan del Río, National Institute of Technology of Mexico, San Juan del Rio Queretaro 76800, Mexico
Wilfrido Campos-Francisco: Technological Institute of Chilpancingo, National Institute of Technology of Mexico, Chilpancingo de los Bravo 39090, Mexico
Carlos Virgilio Marmolejo-Vega: Technological Institute of Chilpancingo, National Institute of Technology of Mexico, Chilpancingo de los Bravo 39090, Mexico
Carlos Marmolejo-Duarte: Center of Land Policy and Valuations, Barcelona School of Architecture (ETSAB), Polytechnic University of Catalonia, 08034 Barcelona, Spain
Yanet Evangelista-Alcocer: Technological Institute of Chilpancingo, National Institute of Technology of Mexico, Chilpancingo de los Bravo 39090, Mexico
Diego Esteban Gutiérrez-Valencia: Technological Institute of Chilpancingo, National Institute of Technology of Mexico, Chilpancingo de los Bravo 39090, Mexico

Sustainability, 2025, vol. 17, issue 8, 1-18

Abstract: 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 delimiting 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.

Keywords: object detection; waste; Convolutional Neural Networks; Raspberry Pi; embedded system; sustainable development; management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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