23279

A Distributed Architecture for Smart Recycling Using Machine Learning

Dimitris Ziouzios, Dimitris Tsiktsiris, Nikolaos Baras, Minas Dasygenis
Department of Electrical and Computer Engineering, University of Western Macedonia, 501 00 Kozani, Greece
Future Internet, 12(9), 141, 2020

@article{ziouzios2020distributed,

   title={A Distributed Architecture for Smart Recycling Using Machine Learning},

   author={Ziouzios, Dimitris and Tsiktsiris, Dimitris and Baras, Nikolaos and Dasygenis, Minas},

   journal={Future Internet},

   volume={12},

   number={9},

   pages={141},

   year={2020},

   publisher={Multidisciplinary Digital Publishing Institute}

}

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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.
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