The Development of an Automated Waste Segregator - Azis, Fatin Amanina; Suhaimi, Hazwani; Abas, Pg Emeroylariffion (International Journal of Integrated Engineering, 2023)
Accumulation of waste is a major global concern, and recycling is considered one of the most effective methods to solve the problem. However, recycling requires proper segregation of waste according to waste types. This paper develops an automatic waste segregator, capable of identifying and segregating six types of wastes; metal, paper, plastic, glass, cardboard, and others. The proposed system employs 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%. Classification performance of the developed waste segregator has 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. These results 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 recyclable waste, through automation.