Waste Classification using Convolutional Neural Network on Edge Devices


Authors : Ashik Mohammed Sali; Harish Thampy; Immanuel S Vadakedam

Volume/Issue : Volume 5 - 2020, Issue 11 - November

Google Scholar : http://bitly.ws/9nMw

Scribd : https://bit.ly/37nbIyL

Waste management is an important issue in the current scenario. Sorting of waste into different categorizes is one of the most important and tedious step in waste management. Normally this is a manual (hand-picking) process which has its own cons and hence the need for an automated and efficient system to manage waste arises. Through this paper we propose an intelligent waste classification system, developed using a deep pre-trained (Xception) Convolutional Neural Network model that can classify solid wastes such as glass, metal, paper and plastic etc. on stand-alone edge devices. This system can be further deployed into a real time embedded system by adding a mechanism to physically separate wastes. The proposed system is trained on an open source dataset available online and is able to achieve a test accuracy of 92% on the dataset. Thus the system could make the separation process faster and intelligent without or reducing human involvement.

Keywords : Convolutional Neural Networks, Machine Learning, Deep Learning, Xception model, Dataset, Training, Sorting, Edge computing.

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