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A Framework for Dry Waste Detection Based on a Deep Convolutional Neural Network

Publish Year: 1399
Type: Journal paper
Language: English
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JR_IJEE-11-4_001

Index date: 21 April 2021

A Framework for Dry Waste Detection Based on a Deep Convolutional Neural Network abstract

Due to lack of proper regulations in many areas of the world, consumers are not mandated to waste sorting at the origin of the source. Moreover, human sorting often suffers from human errors and low accuracy. In the intelligent detection system, it is attempted to break down a variety of household wastes including plastic bottles, glass, metals, paper bags, compact plastics, paper and disposable containers. In this paper, a real waste image system is investigated using the deep convolutional neural network and a remarkable accuracy of 92.76% was achieved.

A Framework for Dry Waste Detection Based on a Deep Convolutional Neural Network Keywords:

A Framework for Dry Waste Detection Based on a Deep Convolutional Neural Network authors

A. Ataee

Department of Electrical Engineering, Babol Noshirvani University of Technology, Babol, Iran

J. Kazemitabar

Department of Electrical Engineering, Babol Noshirvani University of Technology, Babol, Iran

M. Najafi

Department of Electrical and Computer Engineering. Arak University of Technology, Arak, Iran

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