Intelligent and Real-Time Detection and Classification Algorithm for Recycled Materials Using Convolutional Neural Networks
Dimitris Ziouzios; Nikolaos Baras; Vasileios Balafas; Minas Dasygenis; Adam Stimoniaris
In recent years, the production of municipal solid waste has constantly been increasing. Recycling is becoming more and more important, as it is the only way that we can have a clean and sustainable environment. Recycling, however, is a process that is not fully automated: large volumes of waste materials need to be processed manually. New and novel techniques have to be implemented in order to manage the increased volume of waste materials at recycling factories. In this paper, we propose a novel methodology that can identify common waste materials as they are being processed on a moving belt in waste collection facilities. An efficient waste material detection and classification system is proposed, which can be used in real integrated solid waste management systems. This system is based on a convolutional neural network and is trained using a custom dataset of images, taken on site from actual moving belts in waste collection facilities. The experimental results indicate that the proposed system can outperform existing algorithms found in the literature in real-world conditions, with 92.43% accuracy.
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