Solid waste identification and segregation hub (S.W.I.S.H.)

Clicks: 1
ID: 285976
2022
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Abstract
Improper disposal of waste can lead to environmental problems such as pollution that negatively impacts the ecosystem. Proper waste segregation is an essential practice to help maintain the sustainability of the environment. This results in a significant reduction in the number of garbage to be disposed of in landfills. This research aims to develop an automated waste segregator that will classify and sort waste objects according to their group (biodegradable, non-biodegradable, and recyclable). With the help of a CNN model that is trained using transfer learning approaches, computer vision technology made it possible to identify waste objects. 15 waste object classes were specified , 5 objects per group. It was found that the performance of the CNN model is highly dependent on the quality of the dataset and the source model of choice. With experiments, our best performing CNN model was obtained by training a total of 8,621 images from 15 different classes with ResNet-50 as our source model.
Reference Key
persistent_1760657210_68f17f3a38501 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Pimentel, Kyle Daniel P.
Journal Malay Journal
Year 2022
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdb_ece/12
Keywords Keywords not found

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