Automatic waste detection by deep learning and disposal system design

Abstract

As Dubai aims to become a greener city by the year 2021, efforts are currently being made by the government to devise a more efficient and innovative approach to tackling solid-waste-management issues in the city. With a much higher rate of recycling of trash, there arises a need to find a better approach to classifying this trash with increased efficiency. Machine learning techniques can be employed to classify trash into different recycling categories so that it is easier to recycle waste. In this paper, an automatic waste-classification system is proposed using a deep learning algorithm to classify waste as metal, paper, plastic and non-recyclable waste. The classification was performed through this computer vision approach by using the AlexNet convolutional neural network architecture in real time so that the waste can be dropped into the appropriate chambers as soon as it is thrown into dustbins. The data set used to train the system consisted of images collected from the Internet, as well as hand-collected images. The model used was tested for classification of different types of trash and was found to show a high accuracy, as discussed in the result section.