Waste Management Classification using Convolutional Neural Network

DOI

10.2991/978-94-6463-589-8_30How to use a DOI?

Keywords

Waste classification; Image detection; Faster R-CNN; Convolutional Neural Networks; Recycle; Mobile apps

Abstract

Efficient waste management plays a crucial role in ensuring the sustainability of our environment and safeguarding public health. However, the lack of knowledge and awareness of waste management practices among the public will increase waste production and cause an unprecedented environmental crisis. Waste separation is crucial for addressing environmental problems, but the diversity of household and company waste complicates proper separation. This paper aims to develop a user-friendly mobile application to help with waste sorting processes by identifying various waste materials. This waste separation solution includes image detection using Faster Regional Convolutional Neural Networks (Faster R-CNN). The comprehensive study involved meticulously curating and utilizing a dataset of 250 images of five categories, which consist of glass, metal, paper, cardboard, and plastic. After a thorough pre-processing process, the datasets are then used for training data and testing data. The test consists of 7 trials. Remarkably, the outcomes yielded a good confidence value for cardboard and plastic, which are 97.99% and 95.24% respectively. Meanwhile, paper achieved 87.54%, glass achieved 70.24%, and metal achieved 65.88% in confidence value. This means the algorithm can detect and classify successfully the five waste categories. Furthermore, we embedded this best model in an Android application to demonstrate its potential for consumer and industrial usage, underscoring the transformative potential of technology in enhancing waste management practices and fostering environmental sustainability initiatives.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Hafizatul Hanin Hamzah
AU  - Wan Athirah Wan Endut
AU  - Azmina Mohd Ariffin
AU  - Nur Atiqah Sia Abdullah
PY  - 2024
DA  - 2024/12/01
TI  - Waste Management Classification using Convolutional Neural Network
BT  - Proceedings of the International Conference on Innovation & Entrepreneurship in Computing, Engineering & Science Education (InvENT 2024)
PB  - Atlantis Press
SP  - 330
EP  - 340
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-589-8_30
DO  - 10.2991/978-94-6463-589-8_30
ID  - Hamzah2024
ER  -