Capsule Neural Networks and Visualization for Segregation of Plastic and Non-Plastic Wastes

Abstract

Building an image processing model for prediction or classification application has to overcome quite a lot of challenges. Convolutional neural network (CNN) is the pillar of image processing in deep learning perspective. In order to bring down the disadvantages and for improving the performance compared to the CNN, a new architecture of CNN had been devised which is known as Capsule neural network (Capsule-Net). By this paper we analyze Capsule-Net for solid waste management which is separation of plastic and non-plastic. This task is viewed as of at most significance in today's world due to volumes of waste generated and nonavailability of human labor for this work. The capsule-Net is evaluated using 2 different datasets. Dataset 1 represents materials collected from public places and Dataset 2 represents materials collected from private environment. The proposed architecture with capsule-Net gives an accuracy of 96.3% for Dataset 1 and 95.7% for Dataset 2. The necessary hardware setup has been developed and tested. This will be a grace to the society which faces unexplainable difficulty in disposing wastes. It is inexpensive labor free and harmless to health.