Open AccessA Semi Smart Adaptive Approach for Trash Classification
Author(s) -
Faisal Y. Alzyoud,
Mahmoud Maqableh,
Faiz Al Shrouf
Publication year - 2021
Publication title -
international journal of computers, communications and control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.422
H-Index - 33
eISSN - 1841-9844
pISSN - 1841-9836
DOI - 10.15837/ijccc.2021.4.4172
Subject(s) - garbage , computer science , barcode , convolutional neural network , sorting , artificial neural network , artificial intelligence , operating system , programming language
Waste management and recycling play a crucial factor in world economy sustainability as they prevent the squander of useful materials which can lead in garbage landfill reduction and cost reduction respectively. Garbage sorting into different categories plays an important role in recycling and waste management; but unfortunately, most garbage sorting still depends on labor which has a reverse impact on mankind and world economy, so there are different approaches to replace human separation by intelligent machines. In this article, we propose a comprehensive approach, Semi Smart Trash Separator to classify garbage and trash using the following technique: precycling by assigning a barcode or QR code to each material, which will enable the separation process as per assigned code; Magnetic separator helps in collecting conductive metal, then the non-conductive materials are classified according to their hardness. This test is a unique idea used in trash classification. Finally, if there is ambiguity in waste material classification barcode or material properties, the classification will be done using neural network techniques depending on the shapes of trash. Mat lab software is modified to handle convolutional neural networks in the image recognition (AlexNet and GoogLeNet) to be used in the trash classification processes and to test their accuracy. The tests are performed using a trustable data set. The material recognition accuracy rate from the obtained results on AlexNet and GoogLeNet are 75% and 83% respectively.