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

Document Type : Original Article


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

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


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.


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