Particle Size Estimation in Mixed Commercial Waste Images Using Deep Learning

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dc.contributor.authorKittiworapanya, Phongsathorn
dc.contributor.authorPasupa, Kitsuchart
dc.contributor.authorAuer, Peter
dc.date.accessioned2026-08-06T10:32:58Z
dc.date.available2026-08-06T10:32:58Z
dc.date.issued2021-06-29
dc.description.abstractWe assessed several state-of-the-art deep learning algorithms and computer vision techniques for estimating the particle size of mixed commercial waste from images. In waste management, the first step is often coarse shredding, using the particle size to set up the shredder machine. The difficulty is separating the waste particles in an image, which can not be performed well. This work focused on estimating size by using the texture from the input image, captured at a fixed height from the camera lens to the ground. We found that EfficientNet achieved the best performance of 0.72 on F1-Score and 75.89% on accuracy.
dc.identifier.citationACM International Conference Proceeding Series, 2021
dc.identifier.doi10.1145/3468784.3471273
dc.identifier.other2-s2.0-85112176916
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/12141
dc.sourceACM International Conference Proceeding Series
dc.subjectComputer Vision
dc.subjectDeep Learning
dc.subjectMixed Commercial Waste
dc.subjectSize Estimation
dc.subjectWaste Management
dc.titleParticle Size Estimation in Mixed Commercial Waste Images Using Deep Learning
dc.typeConference Paper

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