Cumulative Neural Network Classification and Recognition Technique for Detecting and Tracking of Small-Size Garbage Materials

Abstract

A review of current advanced classification and recognition techniques, as well as recent technological advances in machine vision, shows that the application of a cumulative neural network (the most advanced machine learning algorithm in) for the waste management is an area of research that remains unexplored. The neural network used to prove the notion is referred to as a proof-of-concept neural network. A low-cost method for identifying and classifying recyclables, increase sorting efficiency, lower human workload, and boost to better comprehend the waste data network revolution how complexes of neural networks may change the industry of waste management. Using only color images of input waste, the system was able to classify objects with an accuracy of up to 90° by type of material (paper, glass, cardboard, metal, and plastic). The potential implementation of the recycling algorithm was assessed in terms of economic, social, commercial, and environmental performance, under the concept of integrated and sustainable waste management. When CNN-based systems are compared with existing waste management technologies, it has been found that they have the potential to modify extensive, semi-reversible manufacturer liability programs, and can change. The economy underpins all recycling.