The Development of an Automated Waste Segregator

Abstract

Accumulation of waste isa major global concern,and recycling is considered one of the most effective methods to solve the problem. However, recycling requiresproper segregation of wasteaccording to waste types.This paper developsan automatic waste segregator, capable of identifying andsegregatingsix types of wastes; metal, paper, plastic, glass, cardboard, and others. The proposed systememploys Convolutional Neural Network (CNN) technology, specifically the Inception-v3 architecture, as well as two physical sensors;weight and metal sensors, to classify and segregate the waste. Overall classification accuracy of the system is 86.7%.Classificationperformance of the developed waste segregatorhas been evaluated further using the precision and recall; with high precision obtained for cardboard, metal, and other waste types, and high recall for metal and glass. Theseresults demonstrate the applicability of the developed system in effectively segregating waste at source, and thereby, reducing the need for the commonly labor-intensive segregation at waste facility. Deploying the system has the potential of reducing waste management problems by assisting recycling companies in sorting recyclablewaste, throughautomation.