Dry waste segregation using seamless integration of deep learning and industrial machine vision

Municipal solid waste management has been one of the most critical issues of urban cities today. Increasing population, constructions, industries, etc. are the major factors creating a large amount of waste that is dumped onto the landfill sites. Various systems have been proposed and are under the utilization for the management of municipal waste which includes mechanical vibration-based size-based sorters, eddy current sensor-based sorting of metallic waste, automatic optical waste sorters, etc. This paper focuses on a novel solution for solid waste segregation using the concepts of machine vision and deep learning. The proposed concept is tested for the segregation of solid dry waste particularly plastic bottles, aluminum cans, and tetra packs. The prototype system developed for the segregations works at high speed and accuracy. The prototype system sorts 250 objects per minute with an average accuracy of 96%. The proposed novel idea be extended and implemented for other types of waste segregation and can include more categories of solid dry waste. The system provides a solution for the ever-challenging municipal waste management problem.