The Deep Learning Convolutional Neural Network-Based Intelligent Waste Classification System

Solid trash buildup is a big problem that, if not taken care of properly, could hurt the environment and put people’s health at risk. A smart waste management system is a must if you want to deal with many different kinds of trash. Sorting trash into its different parts is one of the more important parts of garbage management. Most of the time, this is done manually, by “picking.” Our proposed intelligent waste product system for classification consists of a 50-layer Res Net-50 neural networks convolutions model (a machine learning tool that serves as the extractor) and support vector machines (SVM) to classify waste into various categories and types (including metal, glass, paper, and plastic). The proposed technique is put to the test on the garbage photo dataset created by Gary Thungs and Mindyi Yangs, and it passes with a high success rate of 87%. If the suggested garbage categorization system is used, the process of sorting trash will be faster and smarter, with little to no or very little human participation.