Municipal solid waste segregation with CNN

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dc.contributor.authorSrinilta, Chutimet
dc.contributor.authorKanharattanachai, Sivakorn
dc.date.accessioned2026-08-06T10:25:08Z
dc.date.available2026-08-06T10:25:08Z
dc.date.issued2019-07-01
dc.description.abstractPollution from municipal solid waste has been a problem in Thailand for a long time. People generate waste in every minute. Ineffective waste segregation does increase difficulties in solid waste management. The Pollution Control Department of Thailand provides segregation guideline for municipal solid waste. Household wastes should be separated into four types-general waste, compostable waste, recyclable waste and hazardous waste. This paper explored performance of CNN-based waste-type classifiers (VGG-16, ResNet-50, MobileNet V2 and DenseNet-121) in classifying waste types of 9,200 municipal solid waste images. Waste type can be identified directly from waste-type classifier or derived from waste-item class. Derived classifiers outperformed their corresponding direct classifiers in the experiment. The highest waste-type classification accuracy was 94.86% from the derived ResNet-50 classifier.
dc.identifier.citationProceeding 5th International Conference on Engineering Applied Sciences and Technology Iceast 2019, 2019
dc.identifier.doi10.1109/ICEAST.2019.8802522
dc.identifier.other2-s2.0-85071732727
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/10020
dc.sourceProceeding 5th International Conference on Engineering Applied Sciences and Technology Iceast 2019
dc.subjectConvolutional Neural Networks
dc.subjectimage classification
dc.subjecttransfer learning
dc.subjectwaste classification
dc.titleMunicipal solid waste segregation with CNN
dc.typeConference Paper

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