TY  - JOUR
T1  - Classification of recyclable waste using deep learning architectures
AU  - Sevinç, Arzu
AU  - Özyurt, Fatih
PY  - 2022
DA  - October
DO  - 10.5505/fujece.2022.83997
JF  - Firat University Journal of Experimental and Computational Engineering
JO  - FUJECE
PB  - Fırat Üniversitesi
WT  - DergiPark
SN  - 2822-2881
SP  - 122
EP  - 128
VL  - 1
IS  - 3
LA  - en
AB  - Managing waste in big cities is a big problem. Wastes are dangerous in terms of causing environmental pollution and affecting humanhealth. In particular, solid wastes such as glass and plastic do not dissolve in the soil for a long time and pollute the environment. Byrecycling such solid wastes, the surrounding waste can be reduced. Therefore, it is important to classify waste and to recycle theseparated waste. In this study, a data set consisting of 22500 waste images was used. The data set contains color image data with a sizeof 227 x 227 pixels. The data used in the study are divided into two as organic and recyclable waste. This study proposes a deeplearning-based system for classifying waste. With such a system, wastes can be classified and recycled. The data was trained with theResNet 50 architecture and the CNN architecture created to classify waste, and accuracy rates were compared. The CNN architecturecreated to classify waste is more successful for this data set with an accuracy rate of 91.84%.
KW  - Waste classification
KW  - Deep learning
KW  - Convolutional neural network
KW  - ResNet-50 architecture
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UR  - https://doi.org/10.5505/fujece.2022.83997
L1  - http://dergipark.org.tr/tr/download/article-file/2736716
ER  -