Accuracy study of image classification for reverse vending machine waste segregation using convolutional neural network

Mohd Azam, Sazuan Nazrah and Tan, Hor Yan and Md Sani, Zamani and Azizan, Azizul (2024) Accuracy study of image classification for reverse vending machine waste segregation using convolutional neural network. International Journal Of Electrical And Computer Engineering (IJECE), 14 (1). pp. 366-374. ISSN 2088-8708

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Abstract

This study aims to create a sorting system with high accuracy that can classify various beverage containers based on types and separate them accordingly. This reverse vending machine (RVM) provides an image classification method and allows for recycling three types of beverage containers: drink carton boxes, polyethylene terephthalate (PET) bottles, and aluminium cans. The image classification method used in this project is transfer learning with convolutional neural networks (CNN). AlexNet, GoogLeNet, DenseNet201, InceptionResNetV2, InceptionV3, MobileNetV2, XceptionNet, ShuffleNet, ResNet 18, ResNet 50, and ResNet 101 are the neural networks that used in this project. This project will compare the F1- score and computational time among the eleven networks. The F1-score and computational time of image classification differs for each neural network. In this project, the AlexNet network gave the best F1-score, 97.50% with the shortest computational time, 2229.235 s among the eleven neural networks.

Item Type:	Article
Uncontrolled Keywords:	Accuracy analysis, Convolutional neural networks, Image classification, Reverse vending machine, Transfer learning
Divisions:	Faculty Of Electrical Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	04 Feb 2025 16:18
Last Modified:	04 Feb 2025 16:18
URI:	http://eprints.utem.edu.my/id/eprint/28226
Statistic Details:	View Download Statistic

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