A Distributed Architecture for Smart Recycling Using Machine Learning

Dimitris Ziouzios, Dimitris Tsiktsiris, Nikolaos Baras and Minas Dasygenis
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Dimitris Ziouzios: Department of Electrical and Computer Engineering, University of Western Macedonia, 501 00 Kozani, Greece
Dimitris Tsiktsiris: Department of Electrical and Computer Engineering, University of Western Macedonia, 501 00 Kozani, Greece
Nikolaos Baras: Department of Electrical and Computer Engineering, University of Western Macedonia, 501 00 Kozani, Greece
Minas Dasygenis: Department of Electrical and Computer Engineering, University of Western Macedonia, 501 00 Kozani, Greece

Future Internet, 2020, vol. 12, issue 9, 1-13

Abstract: Recycling is vital for a sustainable and clean environment. Developed and developing countries are both facing the problem of solid management waste and recycling issues. Waste classification is a good solution to separate the waste from the recycle materials. In this work, we propose a cloud based classification algorithm for automated machines in recycling factories using machine learning. We trained an efficient MobileNet model, able to classify five different types of waste. The inference can be performed in real-time on a cloud server. Various techniques are described and used in order to improve the classification accuracy, such as data augmentation and hyper-parameter tuning. Multiple industrial stations are supported and interconnected via custom data transmission protocols, along with security features. Experimental results indicated that our solution can achieve excellent performance with 96.57% accuracy utilizing a cloud server.

Keywords: computer vision; object detection; CNN; cloud computing; machine learning; computation offloading; municipal solid waste; recycling (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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